Tooling

There's a lot of buzz around Microsoft Fabric these days. Some people are all-in, singing its praises from the rooftops, while others are more skeptical, waving the "buyer beware" flag. After talking with the community and observing Fabric in action, we're leaning toward caution. Why? Well, like many things in the Microsoft ecosystem, it's a jack of all trades but a master of none. Many of the promises seem to be more marketing hype than substance, leaving you with "marketecture" instead of solid architecture. While the product has admirable, lofty goals, Microsoft has many wrinkles to iron out.

In this article, we'll dive into 10 reasons why Microsoft Fabric might not be the best fit for your organization in 2025. By examining both the promises and the current realities of Microsoft Fabric, we hope to equip you with the information needed to make an informed decision about its adoption.

What is Microsoft Fabric?

Microsoft Fabric is marketed as a unified, cloud-based data platform developed to streamline data management and analytics within organizations. Its goal is to integrate various Microsoft services into a single environment and to centralize and simplify data operations.

This means that Microsoft Fabric is positioning itself as an all-in-one analytics platform designed to handle a wide range of data-related tasks. A place to handle data engineering, data integration, data warehousing, data science, real-time analytics, and business intelligence. A one stop shop if you will. By consolidating these functions, Fabric hopes to provide a seamless experience for organizations to manage, analyze, and gather insights from their data.

Core Components of Microsoft Fabric

• OneLake: OneLake is the foundation of Microsoft Fabric, serving as a unified data lake that centralizes storage across Fabric services. It is built on Delta Lake technology and leverages Azure Blob Storage, similar to how Apache Iceberg is used for large-scale cloud data management

• Synapse Data Warehouse: Similar to Amazon Redshift this provides storage and management for structured data. It supports SQL-based querying and analytics, aiming to facilitate data warehousing needs.

• Synapse Data Engineering: Compute engine based on Apache Spark, similar to Databricks' offering. It is built on Apache Spark and is intended to support tasks such as data cleaning, transformation, and feature engineering.

• Azure Data Factory: A tool for pipeline orchestration and data loading which is also part of Synapse Data Engineering

• Synapse Data Science: Similar to Jupiter Notebooks that can only run on Azure Spark. It is designed to support data scientists in developing predictive analytics and AI solutions by leveraging Azure ML and Azure Spark services.

• Synapse Real-Time Analytics: Enables the analysis of streaming data from various sources including Kafka, Kinesis, and CDC sources.

• Power BI: This is a BI (business intelligence) tool like Tableau tool designed to create data visualizations and dashboards.

Fabric presents itself as an all-in-one solution, but is it really? Let’s break down where the marketing meets reality.

10 Reasons It’s Still Not the Right Choice in 2025

While Microsoft positions Fabric is making an innovative step forward, much of it is clever marketing and repackaging of existing tools. Here’s what’s claimed—and the reality behind these claims:

1. Fragmented User Experience, Not True Unification

Claim: Fabric combines multiple services into a seamless platform, aiming to unify and simplify workflows, reduce tool sprawl, and make collaboration easier with a one-stop shop.

Reality:

• Rebranded Existing Services: Fabric is mainly repackaging existing Azure services under a new brand. For example, Fabric bundles Azure Data Factory (ADF) for pipeline orchestration and Azure Synapse Analytics for traditional data warehousing needs and Azure Spark for distributed workloads. While there are some enhancements to synapse to synchronize data from OneLake, the core functionalities remain largely unchanged. PowerBI is also part of Fabric  and this tool has existed for years as have notebooks under the Synapse Data Science umbrella.

• Steep Learning Curve and Complexity: Fabric claims to create a unified experience that doesn’t exist in other platforms, but it just bundles a wide range of services—from data engineering to analytics—and introduces new concepts (like proprietary query language, KQL which is only used in the Azure ecosystem). Some tools are geared to different user personas such as ADF for data engineers and Power BI for business analysts, but to “connect” an end-to-end process, users would need to interact with different tools. This can be overwhelming, particularly for teams without deep Microsoft expertise. Each tool has its own unique quirks and even services that have functionality overlap don’t work exactly the same way to do the same thing. This just complicates the learning process and reduces overall efficiency.

2. Performance Bottlenecks & Throttling Issues

Claim: Fabric offers a scalable and flexible platform.

Reality: In practice, managing scalability in Fabric can be difficult. Scaling isn’t a one‑click, all‑services solution—instead, it requires dedicated administrative intervention. For example, you often have to manually pause and un-pause capacity to save money, a process that is far from ideal if you’re aiming for automation. Although there are ways to automate these operations, setting up such automation is not straightforward. Additionally, scaling isn’t uniform across the board; each service or component must be configured individually, meaning that you must treat them on a case‑by‑case basis. This reality makes the promise of scalability and flexibility a challenge to realize without significant administrative overhead.  

3. Capacity-Based Pricing Creates Cost Uncertainty

Claim: Fabric offers predictable, cost-effective pricing.

Reality: While Fabric's pricing structure appears straightforward, several hidden costs and adoption challenges can impact overall expenses and efficiency:  

• Cost uncertainty: Microsoft Fabric uses a capacity-based pricing model that requires organizations to purchase predefined Capacity Units (CUs).  Organizations need to carefully assess their workload requirements to optimize resource allocation and control expenses. Although a pay-as-you-go (PAYG) option is available, it often demands manual intervention or additional automation to adjust resources dynamically.  This means organizations often need to overprovision compute power to avoid throttling, leading to inefficiencies and increased costs. The problem is you pay for what you think you will use and get a 40% discount. If you don’t use all of the capacity, then there are wasted capacity. If you go over capacity, you can do configure PAYG (pay as you go) but it’s at full price. Unlike true serverless solutions, you pay for allocated capacity regardless of actual usage. This isn’t flexible like the cloud was intended to be.  👎

• Throttling and Performance Degradation: Exceeding purchased capacity can result in throttling, causing degraded performance. To prevent this, organizations might feel compelled to purchase higher capacity tiers, further escalating costs.

• Visibility and Cost Management: Users have reported challenges in monitoring and predicting costs due to limited visibility into additional expenses. This lack of transparency necessitates careful monitoring and manual intervention to manage budgets effectively.  

• Adoption and Training Time: It’s important to note that implementing Fabric requires significant time investment in training and adapting existing workflows. While this is the case with any new platform, Microsoft is notorious for complexity in their tooling and this can lead to longer adoption periods, during which productivity may temporarily decline.

All this to say that the pricing model is not good unless you can predict with great accuracy exactly how much you will spend every single day, and who knows that? Check out this article on the hidden cost of fabric which goes into detail and cost comparisons.

4. Limited Compatibility with Non-Microsoft Tools

Claim: Fabric supports a wide range of data tools and integrations.

Reality: Fabric is built around a tight integration with other Fabric services and Microsoft tools such as Office 365 and Power BI, making it less ideal for organizations that prefer a “best‑of‑breed” approach (or rely on tools like Tableau, Looker, open-source solutions like Lightdash, or other non‑Microsoft solutions), this can severely limit flexibility and complicate future migrations.

While third-party connections are possible, they don’t integrate as smoothly as those in the MS ecosystem like Power BI, potentially forcing organizations to switch tools just to make Fabric work.

5. Poor DataOps & CI/CD Support

Claim: Fabric simplifies automation and deployment for data teams by supporting modern DataOps workflows.

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Reality: Despite some scripting support, many components remain heavily UI‑driven. This hinders full automation and integration with established best practices for CI/CD pipelines (e.g., using Terraform, dbt, or Airflow). Organizations that want to mature data operations with agile DataOps practices find themselves forced into manual workarounds and struggle to integrate Fabric tools into their CI/CD processes. Unlike tools such as dbt, there is not built-in Data Quality or Unit Testing, so additional tools would need to be added to Fabric to achieve this functionality.

6. Security Gaps & Compliance Risks

Claim: Microsoft Fabric provides enterprise-grade security, compliance, and governance features.

Reality: While Microsoft Fabric offers robust security measures like data encryption, role-based access control, and compliance with various regulatory standards, there are some concerns organizations should consider.

One major complaint is that access permissions do not always persist consistently across Fabric services, leading to unintended data exposure.

For example, users can still retrieve restricted data from reports due to how Fabric handles permissions at the semantic model level. Even when specific data is excluded from a report, built-in features may allow users to access the data, creating compliance risks and potential unauthorized access. Read more: Zenity - Inherent Data Leakage in Microsoft Fabric.

While some of these security risks can be mitigated, they require additional configurations and ongoing monitoring, making management more complex than it should be. Ideally, these protections should be unified and work out of the box rather than requiring extra effort to lock down sensitive data.

7. Lack of Maturity & Changes that Disrupt Workflow

Claim: Fabric is presented as a mature, production-ready analytics platform.

Reality: The good news for Fabric is that it is still evolving. The bad news is, it's still evolving. That evolution impacts users in several ways:  

• Frequent Updates and Unstable Workflows: Many features remain in preview, and regular updates can sometimes disrupt workflows or introduce unexpected issues. Users have noted that the platform’s UI/UX is continually changing, which can impact consistency in day-to-day operations. Just when you figure out how to do something, the buttons change. 😤

• Limited Features: Several functionalities are still in preview or implementation is still in progress. For example, dynamic connection information, Key Vault integration for connections, and nested notebooks are not yet fully implemented. This restricts the platform’s applicability in scenarios that rely on these advanced features.

• Bugs and Stability Issues: A range of known issues—from data pipeline failures to problems with Direct Lake connections—highlights the platform’s instability. These bugs can make Fabric unpredictable for mission-critical tasks. One user lost 3 months of work!

8. Black Box Automation & Limited Customization

Claim: Fabric automates many complex data processes to simplify workflows.

Reality: Fabric is heavy on abstractions and this can be a double‑edged sword. While at first it may appear to simplify things, these abstractions lead to a lack of visibility and control. When things go wrong it is hard to debug and it may be difficult to fine-tune performance or optimize costs.

For organizations that need deep visibility into query performance, workload scheduling, or resource allocation, Fabric lacks the granular control offered by competitors like Databricks or Snowflake.

9. Limited Resource Governance and Alerting

Claim: Fabric offers comprehensive resource governance and robust alerting mechanisms, enabling administrators to effectively manage and troubleshoot performance issues.  

Reality: Fabric currently lacks fine-grained resource governance features making it challenging for administrators to control resource consumption and mitigate issues like the "noisy neighbor" problem, where one service consumes disproportionate resources, affecting others.  

The platform's alerting mechanisms are also underdeveloped. While some basic alerting features exist, they often fail to provide detailed information about which processes or users are causing issues. This can make debugging an absolute nightmare. For example, users have reported challenges in identifying specific reports causing slowdowns due to limited visibility in the capacity metrics app. This lack of detailed alerting makes it difficult for administrators to effectively monitor and troubleshoot performance issues, often needing the adoption of third-party tools for more granular governance and alerting capabilities. In other words, not so all in one in this case.

10. Missing Features & Gaps in Functionality

Claim: Fabric aims to be an all-in-one platform that covers every aspect of data management.  

Reality: Despite its broad ambitions, key features are missing such as:

• Geographical Availability: Fabric's data warehousing does not support multiple geographies, which could be a constraint for global organizations seeking localized data storage and processing.  

• Garbage Collection: Parquet files that are no longer needed are not automatically removed from storage, potentially leading to inefficient storage utilization.  

While these are just a couple of examples it's important to note that missing features will compel users to seek third-party tools to fill the gaps, introducing additional complexities.  Integrating external solutions is not always straight forward with Microsoft products and often introduces a lot of overhead.  Alternatively, users will have to go without the features and create workarounds or add more tools which we know will lead to issues down the road.  

Conclusion

Microsoft Fabric promises a lot, but its current execution falls short. Instead of an innovative new platform, Fabric repackages existing services, often making things more complex rather than simpler.

That’s not to say Fabric won’t improve—Microsoft has the resources to refine the platform. But as of 2025, the downsides outweigh the benefits for many organizations.

If your company values flexibility, cost control, and seamless third-party integrations, Fabric may not be the best choice. There are more mature, well-integrated, and cost-effective alternatives that offer the same features without the Microsoft lock-in.

Time will tell if Fabric evolves into the powerhouse it aspires to be. For now, the smart move is to approach it with a healthy dose of skepticism.

👉 Before making a decision, thoroughly evaluate how Fabric fits into your data strategy. Need help assessing your options? Check out this data platform evaluation worksheet.  

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SQL databases are great for organizing, storing, and retrieving structured data essential to modern business operations. These databases use Structured Query Language (SQL), a gold standard tool for managing and manipulating data, which is universally recognized for its reliability and robustness in handling complex queries and vast datasets.

SQL is so instrumental to database management that databases are often categorized based on their use of SQL. This has led to the distinction between SQL databases, which use Structured Query Language for managing data, and NoSQL databases, which do not rely on SQL and are designed for handling unstructured data and different data storage models. If you are looking to compare SQL databases or just want to deepen your understanding of these essential tools, this article is just for you.

What is an Open Source Database?  

Open source databases are software systems whose source code is publicly available for anyone to view, modify, and enhance. This article covers strictly open source SQL databases. Why? Because we believe that they bring additional advantages that are reshaping the data management space. Unlike proprietary databases that can be expensive and restrictive, open source databases are developed through collaboration and innovation at their core. This not only eliminates licensing fees but also creates a rich environment of community-driven enhancements. Contributors from around the globe work to refine and evolve these databases, ensuring they are equipped to meet the evolving demands of the data landscape.  

Why Use Open Source Databases?

Cost-effectiveness: Most open source databases are free to use, which can significantly reduce the total cost of ownership.

Flexibility and Customization: Users can modify the database software to meet their specific needs, a benefit not always available with proprietary software.

Community Support: Robust communities contribute to the development and security of these databases, often releasing updates and security patches faster than traditional software vendors.

OLTP vs OLAP

When selecting a database, it is important to determine your primary use case. Are you frequently creating, updating, or deleting data? Or do you need to analyze large volumes of archived data that doesn't change often? The answer should guide the type of database system you choose to implement.  

In this article we will be touching on OLTP and OLAP open source SQL databases. These databases are structured in different ways depending on the action they wish to prioritize analytics, transactions, or a hybrid of the two.  

What is OLTP?

OLTP or Online Transaction Processing databases are designed to manage and handle high volumes of small transactions such as inserting, updating, and/or deleting small amounts of data in a database. OLTP databases can handle real-time transactional tasks due to their emphasis on speed and reliability. The design of OLTP databases is highly normalized to reduce redundancy and optimizes update/insert/delete performance. OLTP databases can be used for analytics but this is not recommended since better databases suited for analytics exist.

Characteristics of OLTP:

• Handles large numbers of transactions by many users.
• Operations are typically simple (e.g., updating a record or retrieving specific record details).
• Focus on quick query processing and maintaining data integrity in multi-access environments.
• Data is highly normalized.

When to use OLTP?

Use OLTP if you are developing applications that require fast, reliable, and secure transaction processing. Common use cases include but are not limited to:  

E-commerce: Order placement, payment processing, customer profile management, and shopping cart updates.

Banking: Account transactions, loan processing, ATM operations, and fraud detection.

Customer Relationship Management (CRM): Tracking customer interactions, updating sales pipelines, managing customer support tickets, and monitoring marketing campaigns.

What is OLAP?

OLAP or Online Analytical Processing databases are designed to perform complex analyses and queries on large volumes of data. They are optimized for read-heavy scenarios where queries are often complicated and involve aggregations such as sums and averages across many datasets. OLAP databases are typically denormalized, which improves query performance but come with the added expense of storage space and slower update speeds.

Characteristics of OLAP:

• Designed for analysis and reporting functions.
• Queries are complex and involve large volumes of data.
• Focus on maximizing query speed across large datasets.
• Data may be denormalized to expedite query processing.

When to use OLAP?

Use OLAP if you need to perform complex analysis on large datasets to gather insights and support decision making. Common use cases include but are not limited to:

Retail Sales Data Analysis: A retail chain consolidates nationwide sales data to analyze trends, product performance, and customer preferences.

Corporate Performance Monitoring: A multinational uses dashboards to track financial, human resources, and operational metrics for strategic decision-making.

Financial Analysis and Risk Management: A bank leverages an OLAP system for financial forecasting and risk analysis using complex data-driven calculations.

In practice, many businesses will use both types of systems: OLTP systems to handle day-to-day transactions and OLAP systems to analyze data accumulated from these transactions for business intelligence and reporting purposes.

Now that we are well versed in OLTP vs OLAP, let's dive into our open source databases!  

Open Source OLTP Databases

PostgreSQL

A row-oriented database, often considered the world’s most advanced open source database. PostgreSQL offers extensive features designed to handle a range of workloads from single machines to data warehouses or web services with many concurrent users.

Best Uses: Enterprise applications, complex queries, handling large volumes of data.

SQLite

SQLite is a popular choice for embedded database applications, being a self-contained, high-reliability, and full-featured SQL database engine. This database is a File-based database which means that they store data in a file (or set of files) on disk, rather than requiring a server-based backend. This approach has several key characteristics and advantages such as being lightweight, portable, easy to use, and self-contained.

Best Uses: Mobile applications, small to medium-sized websites, and desktop applications.

MariaDB

A columnar database and offshoot of MySQL. MariaDB was created by the original developers of MySQL after concerns over its acquisition by Oracle. It is widely respected for its performance and robustness.

Best Uses: Web-based applications, cloud environments, or as a replacement for MySQL.

Firebird

Firebird is a flexible relational database offering many ANSI SQL standard features that run on Linux, Windows, and a variety of Unix platforms. This database can handle a hybrid approach of OLTP and OLAP due to its multi-generational architecture and because readers do not block writers when accessing the same data.

Best Uses: Small to medium enterprise applications, particularly where complex, customizable database systems are required.

Open Source OLAP Databases

ClickHouse

Known for its speed, ClickHouse is an open-source column-oriented, File-based database management system that is great at real-time query processing over large datasets.  As mentioned earlier in the article, File-based databases bring many benefits They make use of data compression, disk storage of data, parallel processing on multiple cores, distributed processing on multiple servers and more.

Best Uses: Real-time analytics and managing large volumes of data.

DuckDB

Similar to SQLite, DuckDB is an embedded file-based database, however, DuckDB is a column-oriented database that is designed to execute analytical SQL queries fast and efficiently. This database has no dependencies making it a simple, efficient, and portable database. Since it is file-based this means DuckDB runs embedded within the host process, which allows for high-speed data transfer during analytics.

Best Uses: Analytical applications that require fast, in-process SQL querying capabilities.

StarRocks

StarRocks is a performance-oriented, columnar distributed data warehouse designed to handle real-time analytics. StarRocks also supports hybrid row-column storage. It is known for its blazing-fast massively parallel processing (MPP) abilities. Data can be ingested at a high speed and updated and deleted in real time making it perfect for real-time analytics on fresh data.

Best Uses: Real-time analytical processing on large-scale datasets.

Doris

Doris is an MPP-based, column-oriented data warehouse, aimed at providing high performance and real-time analytical processing. Doris can support highly concurrent point query scenarios and high-throughput complex analytic scenarios. Its high speed and ease of use despite working with large amounts of data make it a great option.

Best Uses: Real-time OLAP applications and scenarios demanding fast data processing and complex aggregation.

Trino

Even though Trino is not a database, but rather a query engine that allows you to query your databases, we felt it is a powerful addition to this open source list. Originally developed by Facebook and known as PrestoSQL, Trino is designed to query large data warehouses and big data systems rapidly. Since it is great for working with terabytes or petabytes of data it is an alternative to tools such as Hive or Pig. However, it can also operate on traditional relational databases and other data sources such as Cassandra. One major benefit is that Trino allows you to perform queries across different databases and data sources. This is known as query federation.  

Best Uses: Distributed SQL querying for big data solutions.

Citus

While this is not a separate open source database, we felt it was a good addition to the list because Citus is an extension to PostgreSQL that that transforms your Postgres database into a distributed database. This enables it to scale horizontally.

Best Uses: Scalable PostgreSQL applications, especially those needing to handle multi-tenant applications and real-time analytics over large datasets.

Conclusion  

Open source SQL databases provide a variety of options for organizations and developers seeking flexible, cost-effective solutions for data management. Whether your needs are for handling large data sets, real-time analytics, or robust enterprise applications, there is likely an open source database out there for you.

Data is in the spotlight as companies everywhere realize data's true potential. With big initiatives like GenAI and sophisticated data ecosystems, ensuring data quality is not just a necessity but a mandatory investment for businesses and analysts worldwide. Some people are learning the hard way that you need stable data foundations to get the results these initiatives promise.

While there are many great tools out there, the spotlight on open source tools has never been brighter. Open source software offers transparency, adaptability, and community-driven enhancements that are crucial in the rapidly evolving data landscape. This article covers 5 open source data quality tools and is current as of April 2024, so if that is something that interests you, stick around.

What is data quality?

First things first, what is data quality?  There are many definitions of data quality, but data is considered high quality if it is fit for its intended uses in operations, decision-making, and planning. In other words, data quality refers to the data's accuracy, completeness, reliability, relevance, and how up-to-date it is. In the context of data-driven decision-making, high-quality data is crucial as it directly impacts the accuracy of insights and the effectiveness of decisions. Our data foundation.

High-quality data should be:

Accurate: Data that is free from errors and discrepancies.

Complete: Data that covers the necessary breadth and depth needed by the business.

Reliable: Data that has no missing elements and is consistently represented and sourced.

Relevant: Data that is applicable to the context and purposes for the business.

Current: Data that is up-to-date and timely for its purpose.

Understanding what is needed for data quality is the first step toward recognizing the importance of these tools and practices that maintain or enhance this quality.

What should you track for good data quality?

Now we know what constitutes high quality data but what do we need to monitor to ensure that our data is high quality? The good news is these metrics tend to be universal. For maintaining high data quality, several metrics and elements should be monitored regularly:

Accuracy: Ensure that your data correctly represents reality or the source from which it came.

Completeness: Check for missing values or data segments that could lead to incorrect analysis or conclusions.

Consistency: Data across different systems or platforms should match and be consistent.

Timeliness: Data should be updated and available in a timeframe that aligns with its intended use.

Validity: Data should adhere to the relevant rules, such as data formats and value ranges.

Uniqueness: No duplicates should be present unless necessary, ensuring each entry is unique.

Integrity: There should be a relationship between datasets and records that maintains data accuracy and consistency.

By tracking these metrics, organizations can set up the essential data foundation and significantly improve the trustworthiness and utility of their data. This will lead to better outcomes and insights that can support great data initiatives of the future.  

Since it is essential to track these metrics, companies are on the search for the best tool to help them improve their data quality. Here is a list of open source tools that can be leveraged to improve data quality.

Why open source?

Before we jump into the tool list you may have noticed that a quick google search for this topic will give me many different lists. How is our list different? Well, we are focusing on open source tools. There are many great tools out there both paid and “free” and we put quotes around free because there is no such thing as free; there are always hidden costs (hours worked) for setup and maintenance. However, we wanted to make this open source tool list because regardless of the hidden costs we believe in the following benefits of open source tools:

Transparency: Open source tools offer complete transparency in their operations and algorithms. Users can inspect, modify, and improve the code, which enhances trust and reliability.

Community: Open source projects benefit from the collective intelligence of a global community. This not only accelerates innovation and bug resolution but also provides a large pool of knowledge and support.

Flexibility: With open source, organizations are not locked into proprietary systems, allowing them to tailor tools to their specific needs and integrate them seamlessly into their existing environments.

Cost-effectiveness: While open source doesn't always mean free, it significantly reduces costs associated with licensing fees and vendor lock-in, making cutting-edge tools accessible to everyone.

Quality and Security: Continuous contributions and scrutiny by the community mean that open source tools often meet high standards of quality and security, with issues being identified and addressed rapidly.

Our selection of open source data quality tools is grounded in rigorous open source criteria. We believe that the strength of an open source project lies not just in its ability to solve complex problems but also in its community, transparency, and commitment to ongoing improvement. When compiling this list, we considered factors such as active community engagement, frequency of updates, the quality of the documentation, and ease of contribution. This ensures that the tools recommended not only meet high standards of performance and reliability but also embody the principles that make open source software a valuable asset to the data quality landscape. So without further ado, let's jump into our list.

Open source data quality tool list

dbt Core

Self managed or in Datacoves

Primary Language: SQL / YAML

Purpose & Features: dbt core is an open source tool that allows data analysts and engineers to transform data in their data warehouses by writing dynamic SQL queries, which dbt then converts into tables and views. It also supports version control, testing, and documentation, which helps maintain data integrity and reliability.  

For data quality, dbt Core has some out of the box data tests which can be extended through custom made test, or by using libraries such as dbt-expectations and elementary. Testing is easily done by configuring macros in YAML files or by writing custom SQL tests. However, integrating dbt Core into your data stack can be a big task especially when it comes to scheduling. A managed dbt Core platform such as Datacoves could be a great option for saving time and money. While dbt handles only the 'T' in ELT, Datacoves’ managed dbt Core Platform ensures that the entire ELT process is smooth and interconnected, allowing your team to concentrate on deriving insights from the data. There are other dbt alternatives on the market that can also be explored which handle the "T" in the ELT process.

Who it is for: Best for teams using SQL who want to transform data directly in the warehouse and who want to follow software development best practices including unit testing in their data pipelines.

Soda Core

Primary Language: YAML

Purpose & Features: Soda Core is the open source component that allows users to define data quality checks in code and integrate them into workflows.  

Who it's for: Teams that need data quality checks integrated into their existing Python workflows or data pipelines.

Great Expectations

Primary Language: Python  

Purpose & Features: This tool is a data quality platform that allows you to create data tests, documentation, and profiles automatically. It easily integrates into existing data processing pipelines to ensure data validation against expectations (unit tests). You can collaborate with nontechnical stakeholders by sharing the Data Docs. Data docs are Expectations, Validation Results, and other metadata translated into a human readable format as seen in the image below.  

Who it's for: Data teams looking for a Pythonic way to enforce data quality rules and create automated data documentation.

Deequ

Primary Language: Scala (for Apache Spark)

Purpose & Features: Deequ is an open source tool by Amazon with which you can define "unit tests" (columnar or row level) for large-scale data within the Spark ecosystem. It allows for automated checks of data quality metrics such as completeness, uniqueness, and conformity. This enables data teams to find errors early before they are consumed downstream. You can use Deequ to define your assumptions about the data in unit tests to catch any data that does not meet your assumptions. This tool works on tabular data such as CSV files, databases tables, logs and flattened JSON files.  

Who it's for: Data engineers and scientists working with big data in Spark (billons of rows), particularly those focused on maintaining data quality at scale.

Data Diff

Primary Language: You don’t manually write data quality tests but as you make changes to your SQL data diff will work its magic.  

Purpose & Features: This tool is a little different from the rest because you're not exactly writing tests to catch data quality issues. Instead, this open source Python package by Datafold lets you do development testing by spotting the differences between tables whenever you tweak your code. It's a great way to compare what's happening in your production data against your development changes, helping you see directly how those code changes are playing out in the data.  

Who it's for: Data engineers and teams who need to ensure that changes in data processing and ETL logic do not negatively affect data quality.

Choose the right open source data quality tool for your use case

The concept of "the best" for data quality tools is inherently tied to specific use cases. What might be an ideal solution for one organization could be less effective for another, depending on the unique challenges and requirements each face.

Before you dive into a tool, it's crucial to understand your organization's specific data quality challenges. Are you dealing with high volumes of data, requiring scalability? Or are your main issues related to data consistency and accuracy in a smaller, more controlled dataset? Identifying your primary use case will help you navigate through our top 10 tools and select the one that best fits your situation.  

Here’s 6 tips to help you make an informed choice for your specific needs:

1. Assess Your Data Quality Needs:

Identify the primary issues you're facing with your data. Are you struggling with incomplete data, inconsistencies, outdated information, or data that's not in the right format? Understanding your main challenges will guide you toward a tool that specializes in addressing those specific problems. Once you understand your data quality challenges and objectives, match these with the strengths of the tools listed above

2. Consider Your Technical Environment:

Evaluate the technical stack you are currently using. Some data quality tools are better suited for certain environments or integrate more seamlessly with specific databases, data lakes, or processing frameworks. Choose a tool that aligns with your existing infrastructure to reduce integration headaches.

3. Evaluate Community and Support:

The strength of an open-source tool lies in its community. Look for a tool with an active community, which is evident through regular updates, vibrant forums, and extensive documentation. A strong community can provide invaluable support, from troubleshooting to best practices.

4. Check for Flexibility and Scalability:

Your data needs will evolve, so it’s important to choose a tool that is flexible and can scale with your business. Assess the tool’s ability to handle different data volumes, types, and sources. A good open-source tool should not only solve your current data quality issues but also adapt to future challenges.

5. Review Security and Compliance Features:

Data security and compliance are imperative. Be sure the tool complies with data protection regulations and offers security features to protect your data. This is especially important if you're dealing with sensitive or personal information.

6. Test Drive the Tool:

Finally, don’t hesitate to get your hands dirty. Most open-source tools are free to use, so take advantage of this by testing the tool with your data. This will give you a clear idea of the tool’s usability, effectiveness, and fit with your use case. Be sure to go into this with an open mind to get the most out of the tool.

Conclusion

In the era of generative AI and other lofty initiatives high-quality data is not just an option but a necessity, and embracing these open-source data quality tools can significantly enhance the reliability and accuracy of your data. Remember, the "best" tool is one that aligns closely with your specific use case offering the features and flexibility your team needs to effectively tackle your data quality challenges; it very well could be a combination of these tools. Whether you are in the world of SQL, Python, or any other programming language, there is a tool tailored to your needs. Consider factors such as ease of integration into your current data ecosystem, the learning curve for your team, and the level of community support available.

Data teams deciding on data transformation tools need to consider various aspects before deciding on how they will develop and orchestrate data pipelines. They also need to accelerate infrastructure deployment to deliver at the pace the business requires. 

The hurdle to overcome is that doing this well requires a lot of rethinking of legacy processes and technology. 

Implementing DataOps, CI/CD, and setting up an ETL or ELT isn’t a straightforward process, which is why teams often go with an incremental approach or set up the basics and end up with technical debt that accumulates substantially over time. 

In this article, we’ll go through a list of 10 data transformation tools that will help you get the job done. If you are in the process of evaluating your next ETL/ELT platforms, this article for you.

Side Note: As data professionals, we’ve been around since the early days of data transformation and noticed many flaws within the entire process. There’s a steep learning curve: adding a single tool to the workflow can quickly multiply into a tech stack with multiple SaaS platforms. That’s why we built Datacoves to help you bring everything together to accelerate time to value. If you’d like to learn more about how Datacoves helps you develop and orchestrate data pipelines, you can schedule a free demo here. 

Data transformation in the End-to End process

Data transformation is the process of converting data from one format or structure to another. It improves the performance of data processing systems and compliance with data governance regulations.  

Data transformation is just one of the steps on the road to deriving value from data.

The end-to-end process includes the following steps:

• Data Extraction: To extract data from sources such as databases and APIs 
• Data Loading:  To load data into a desired destination such as a Data Warehouse.
• Data Transformation: To cleanse and transform data into usable insights based on business needs. 
• Data Orchestration: To schedule and automate the end-to-end process
• Data Delivery: To visualize and support decision-making 
• Data Observability: To view and get alerted when data issues occur

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It’s worth taking each of these steps into consideration when determining the best data transformation tool for your organization.

There is a common misconception that the tool alone will solve all the problems. 

However, using the right tools without addressing the underlying processes can lead to a data mess that can exacerbate the underlying issue, costing more time and money. This data mess could easily be avoided in the first place, not just by having the right tools but by also having the modern best practices in place.

What is the difference between ETL and ELT? 

Both help businesses extract, load, and transform data, but the sequence of events is different with their pros and cons.

• ETL Process: The traditional approach to data transformation. Data is Extracted and Transformed before it gets Loaded. 
• ELT Process: The modern approach to data transformation. Data is Extracted and Loaded before it gets Transformed. 

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ELT is generally more effective than ETL processes because it removes the uncertainty of not having the necessary data for future use cases and offers more flexibility in the long term. Since storage is typically affordable, it makes more sense to simplify the ingestion process.

10 best data transformation tools

Here’s a list of the top data transformation tools to manage the ETL process: 

• Datacoves
• dbt Cloud
• Apache Airflow
• SAS
• SQLMesh
• Informatica
• Talend
• Azure Data Factory
• Matillion
• Alteryx

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Each of these tools falls into one of two categories: code-based or visual/drag-and-drop interface. Both have their own set of pros and cons, which we’ll go through below. 

Code-based tools for data transformation 

Code-based tools allow you to transform data by using SQL or Python to explicitly define the transformation steps. Although it requires knowledge and experience, visual tools don’t negate the need to know SQL. This approach gives users a high degree of flexibility and control, and simplifies the maintainability and validation of work before releasing it to production. 

Moreover, it is simpler to trace each data transformation step without having a disconnected document explaining what the transformation “should” do.

1. Datacoves

After having multiple conversations with data teams at enterprise companies, the challenge of developing and orchestrating dbt pipelines is a topic that has come up on numerous occasions. 

There are a lot of tools to figure out when it comes to implementing the best practices for digital transformations and custom applications. It’s not uncommon for companies to end up with more than one SaaS platform and tool than they had initially planned. We built Datacoves to eliminate this need by providing the following: 

• Managed dbt Core
• Open-source technologies, meaning there is no vendor lock-in
• Managed SaaS or private cloud deployment

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Datacoves focuses on helping companies accelerate growth by providing a complete ELT solution, including orchestration and visualization. Therefore, the learning curve for data transformation is minimized because of our best-practice accelerators and the available tool integrations to form an end-to-end platform. 

Top features

• Managed dbt Core: Get full access to dbt through Datacoves, where we provide a structured process for developing dbt pipelines. Configure a dbt environment where you can write data transformations in modular code so it’s easier to test and maintain. 
• Hosted dbt Documentation: Simplified dbt docs deployment
• Managed Airflow: Orchestrate data using Airflow, which is an integration that’s available to give companies the flexibility they need for an end-to-end process from data load to activation.
• VS Code in Browser: No software installation is required, allowing you to write and edit code from anywhere as long as you have access to a web browser.
• Deployable in Private Cloud: Accelerate data transformation and minimize ownership costs while complying with corporate and governmental regulations. 
• Internal Tool Integrations: Integrate with internal tools like Active Directory, Bitbucket, Jenkins, GitLab, and more. 
• CI/CD: Deliver high-quality data to users faster with more reliability and efficiency. 

How data transformation works with Datacoves

Here is the extended version of the ELT process with Datacoves: 

• Extract data
• Load data
• Transform data
• Observe
• Orchestrate
• Analyze 

Develop modular code and track version changes that you and your team can view. You’re also able to validate the quality of data transformations with our built-in testing frameworks and generate documents to leave a record of how you’re transforming data.

You develop in a VS Code environment that can be configured with a vast array of VS Code extensions and Python libraries All the modern data tools you need are provided in a structured workspace: 

• GitHub workflows
• Automation scripts
• Loading
• Scheduling
• Data security
• Data transformation 

Is Datacoves right for you?

It’s suitable for medium and large companies that lack the expertise or don’t want to create and manage complex data processes and need the flexibility that complex enterprise processes require. 

Data teams can use all the components provided within the dbt ecosystem in a structured, methodical way with Datacoves. This means you’ll have a simplified dbt experience, yet you’ll still see the same results of dbt when used to its full potential. 

Smaller companies also gain competitive advantages with Datacoves because they’ll be able to implement DataOps, follow best practices, and get a fully managed VS Code environment accelerating time to value. 

If you would like to know more about how Datacoves can help, you can schedule a demo here. 

2. dbt Cloud

dbt Cloud allows businesses to build and maintain data pipelines. It’s a cloud-based platform with a web-based IDE that allows you to transform data within a cloud data warehouse. They can help you reduce the time spent setting up an end-to-end solution.

Notable features 

• Modular Code: Write data transformations in modular code. 
• Version Control: Monitor changes to your code and seamlessly collaborate with team members. 
• Data Testing: Built-in testing framework for validating the quality of data transformations. 
• Documentation: Generate documents for data transformations so you know how your data is being transformed. 
• CI/CD: Streamline the data transformation process. 

Is dbt Cloud right for you? 

dbt Cloud works well for organizations looking to reduce the time and effort required to transform data pipelines. 

Since dbt Cloud is a web-based IDE, it may feel limited for data teams that would rather use a VS Code environment. Moreover, dbt is not deployable in a company’s private cloud. It also typically requires other SaaS tools for complicated data pipelines, making it more difficult to manage unless you have the necessary integration experience with each of those SaaS tools. 

Most importantly, dbt Cloud is focused solely on the data transformation step of the ELT process. Hence, you are unable to load VS Code extensions nor additional Python libraries. An enterprise with any level of complexity will also need a full-featured orchestrator. 

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3. Apache Airflow

Apache Airflow is an open-source platform for workflow management. You can orchestrate and schedule data pipelines. It’s a scalable and flexible platform that’s based on Python. You can also define your own operators with Airflow. 

Notable features 

• ELT pipelines: Airflow is a tool for organizing full ELT pipelines. But, it still requires your expertise to build them using Python. 
• CI/CD tools: You can use CI/CD tools to assist with deployment. 
• Data Extraction and Load: With airflow, you can create Python scripts that extract and load information from other data sources. 
• Python Libraries: Airflow can be paired with Python libraries like Pandas to shape and aggregate data.
• Machine learning: Schedule the training of machine learning models. 

Is Apache Airflow right for you?

Apache Airflow works well for those needing a scalable data transformation tool with an open-source platform. It’s particularly a good choice for businesses mainly using Python to manage their data. 

However, Airflow is primarily an orchestrator. That means you may end up building complex code in your data pipelines. Therefore, developing and maintaining this complexity requires experience and technical expertise. Managing the infrastructure for Airflow is not trivial and also requires an understanding of tools like Docker and Kubernetes.

4. SAS

SAS is a solution that allows you to transform and prepare data for analysis. It offers a wide range of features for data transformation, including data cleaning, data integration, and data mining.

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Notable features

• Data Cleaning: Remove duplicate records, correct errors, complete missing values, and so forth. 
• Data Integration: Combine data from different departments or systems into a single dataset. 
• Data Mining: Identify patterns and trends in your data. 
• Data Visualization: Create charts and graphs to visualize data. 

Is SAS right for you?

SAS is ideal for companies with complex datasets, such as those in financial services, healthcare, and retail industries. Additionally, it’s ideal for professionals with advanced skills and knowledge in data transformation. 

With that in mind, there are better solutions than SAS for those less experienced in programming and data management, as SAS licensing can be quite expensive.  

5. SQLMesh

SQLMesh is a complete DataOps solution for data testing and transformation. Teams can use SQLMesh to collaborate on data pipelines when transforming data. 

Notable features

• Semantic Understanding: SQLMesh can understand the SQL written so you can write code efficiently and avoid errors.
• Simplified CI/CD: SQLMesh can identify the changes made to data pipelines and apply only the necessary updates to each environment.‍
• Column-level Lineage: Get a better understanding of the relationships between your data and the transformation process.‍
• Transpilation: Run your SQL on multiple engines so that it’s easier to migrate data into a new platform.

Is SQLMesh right for you?

SQLMesh is well-suited for businesses with SQL and Python expertise that need to collaborate on complex data transformations and pipelines. Although other open-source tools are available, teams can use SQLMesh to maintain data quality and perform unit testing of their transformations.

SQLMesh may not be ideal when you only need to perform simple data transformations. In this case, there are other more straightforward tools available. Moreover, SQLMesh may not be for you when your primary focus is on real-time data processing.

Visual ELT tools for data transformation

Visual tools make the ELT process more straightforward by removing the need to manually write code. It works by dragging and dropping pre-built components into a canvas. This makes them ideal for data teams who aren’t as experienced in programming. 

The biggest advantage of graphical tools for ETL is that people who are less comfortable with code can use them. Conversely, drag-and-drop tools typically don’t offer the same level of flexibility and control as code-based tools, which can complicate the process of debugging data pipelines and long-term maintenance.

6. Informatica

Informatica helps you turn your data into an asset. It’s a cloud-based or on-prem solution for data management with numerous data transformation libraries and APIs available. 

Notable features 

• PowerCenter: Enterprises can use this to manage large and complex data pipelines. 
• Cloud Data Integration: Cloud-based integrations that allow you to move data. 
• Data Engineering Integration: A solution designed to assist data engineers with code development, version control, and CI/CD.
• Data Engineering Streaming: Manage streaming data pipelines with data ingestion, processing, and visualization. 

Is Informatica right for you?

Informatica can be a good choice for large enterprises and data professionals looking to quickly transform large volumes of complex data using an on-premise solution. It can also be a good choice for companies that need to comply with industry-specific data standards. 

However, it may be too complicated to use for some organizations. Informatica requires a team of experienced data engineers with the necessary skills and experience. DataOps can also be a challenge. Since you’ll be dealing with multiple things simultaneously, it’s easy to get lost in the process when you don’t have the full technical expertise.

Moreover, it’s an expensive solution. There are other more affordable alternatives.

7. Talend

Talend is a cloud-native platform deployable on public cloud solutions such as AWS, Azure, and GCP. They also offer an on-prem solution and provide a variety of components and custom connectors for data transformation. 

Notable features 

• Talend Open Studio: An open-source data integration tool for smaller workloads. 
• Talend Data Fabric: Manage the data integration process. Maintain data quality and governance. 
• Cloud Data Integration: Cloud-based data integration service with a graphical user-friendly interface for creating and managing data transformation tasks. 
• Built-in Data Catalog: Discover new data assets across your organization. 

Is Talend right for you?

Talend works for most businesses and data professionals. It’s particularly well-suited for those who need to:

• Transform data from a variety of sources.
• Migrate data to a new system. 
• Build and maintain a data warehouse. 
• Check and resolve data quality issues 

Still, you may want to consider other options when prioritizing DataOps and performing highly specialized data transformations such as machine learning or NLP. Talend enterprise licenses may also be costly.

8. Azure Data Factory

Azure Data Factory helps you simplify the data transformation process at scale. You’re provided with a code-free and code-centric experience for orchestrating data transformation pipelines. 

Notable features 

• Built-in connectors: A variety of built-in connectors for popular data sources are available. 
• Data Orchestration: Schedule your data pipelines.
• Built-in Components: Use built-in components to reshape data.

Is Azure Data Factory right for you?

Azure Data Factory could be the right option for data professionals working within the Azure ecosystem. Azure may be worth considering when you’re looking into data warehousing using Azure Synapse and Azure DataOps and not just ELT. 

However, Azure Data Factory might not be the best option when you’re on a budget. As with any visual ELT tooling, DataOps and pipeline maintainability may be more complex leading to an increased total cost of ownership.

9. Matillion

Matillion is a cloud-based data transformation tool that provides you with on-premises databases, cloud applications, and SaaS platform integrations. 

Notable features

• Cloud-native architecture: Matillion runs in the cloud and allows you to push down transformation logic to leverage the scalability and performance of cloud data platforms such as Amazon Redshift and Snowflake. 
• Visual Interface: Create data pipelines using a graphical interface, reducing the need to write code. 
• Library of Connectors: Access a library of pre-built transformations and connectors across a range of data sources. 
• High-Code and No-Code: Supports both hide-code and no-code development, making it accessible for beginner and intermediate users. 
• dbt Component: With the dbt component, you can embed dbt within a Matillion pipeline.

Is Matillion right for you?

Matillion’s pre-built connectors and visual interface makes it an ideal solution for less experienced data professionals. The disadvantage is that it can be costly for businesses on a budget. Moreover, you must ensure that Matillion supports your specific requirements and how you intend to perform the data transformations. Care must be given to the long-term maintainability of pipelines that are both visual and code-based.

Getting started with Matillion is simple because they use a drag-and-drop interface for building data pipelines. But like with any other visual tool, there is still a learning curve and it’s typical to have a mix of code and visual components in a production data pipeline.

10. Alteryx

Alteryx simplifies the data transformation process. You can automate advanced analytics and prepare data through self-service. It’s an effective solution that makes it easier for teams to collaborate. Unlike the other visual tools above which are typically used by Data Engineers in IT, Alteryx is more widely adopted in less technical departments of an organization. It’s also typically paired with visualization tools like Tableau. 

Notable features 

• Drag-and-drop User Interface: The visual interface makes building and collaborating on data transformation workflows easier.
• Data Loading: Connectors to popular databases and services allow you to integrate different data sources 
• Machine Learning: Alteryx may also be used to create simple machine learning models in a visual way

Is Alteryx right for you?

Alteryx is a good option to help ensure teams are on the same page throughout the data workflow. Data transformation projects can be shared and feedback provided seamlessly, making collaboration easier. 

The downside is that Alteryx is costly compared to the other tools on this list. Moreover, there is still a bit of a learning curve, even if you’re experienced in data analytics. You should also check that Alteryx aligns with teams for effective collaboration.

How Datacoves can help you transform data

Data transformation is a process that’s prone to multiple errors along the way. While many tools listed can help you reduce friction, they must be carefully evaluated. With Datacoves, you’ll be able to implement best data practices and DataOps so that you have a smooth process with a minimized learning curve. 

If you’d like to learn more about how Datacoves helps you accelerate time to value, you can schedule a free demo here.

I read an article on Anchor Data Modeling, more specifically, Atomic modeling where the author proposes a different way of Data Modeling. The rationale for this change is that there is a lack of skills to model data well. We are giving powerful tools to novices, and that is bound to lead to problems.

From the article:

"we are in a distressful situation both concerning the art as a whole but also its place in modern architectures"

Is this the case? Do we have a big problem on the horizon that requires us to make this big shift?

I'd say I am open-minded and expose myself to different ways of thinking so I can broaden my views. A few years ago, I learned a bit about COBOL, not because I had any real use for it but because I was curious. I found it very interesting and even saw its similarities with SQL. I approached the topic with no preconceived ideas; this is the first time I heard of Atomic Modeling.

Is there something wrong with atomic and anchor data modeling in general?

The issues I see with ideas like Atomic data modeling are not in their goal. I am 100% aligned with the goal; the problem is the technology, process, and people needed to get there.

What we see in the market is a direct result of a backlash against doing things "perfectly." But why is this the case?  I believe it is because we haven't communicated how we will achieve this vision of ideas like atomic data. The author even says a key phrase in the first paragraph:

"practitioners shying away from its complexity"

If doing anchor data modeling is "complex" how are we going to up-skill people? Is this feasible? I am happy if I can get more people to use SQL vs a GUI tool like Alterix 😁

I am by no means an expert. Yet, I am fairly technical, and if I am not convinced, how will we convince decision-makers?

As I read this article, here's what I am thinking:

1. First, I will need to deconstruct the data I get from some source like material master data form SAP. That will be a bunch of tables, and who is going to do all this data modeling? It sounds expensive and time-consuming.

2. I am going to need some tooling for this, and I am either going to build it or use something a few others are using. Will my company want to take a chance on something this early? This sounds risky.

3. After I deconstruct all this data, I need to catalog all these atoms. I now have a lot of metadata, and that's good, but is the metadata business-friendly?  We can't get people to add table descriptions how is this going to happen with this explosion of objects? Who will maintain it? How will we expose this? Is there a catalog for it already? Does that catalog have the other features people need? It sounds like I need to figure out a bunch of things, the biggest one being the change management aspect.

4. What sort of database will I use to store this? This is a great use case for a graph database. But graph databases are not widely adopted, and I have seen graph databases choke at scale. We can use a relational database, but these joins are going to be complex.  Someone may have figured all this out, but there's more tech and learning needed. It sounds like this will also add time and expense.

5. When I have managed to do all the above, I will need to construct what people can actually use. We need objects that work with tools that are available. I need to make relational tables I can query with SQL and viz tools, which are more user-friendly. This sounds like more work, more time, and more money.

I may have missed some steps and oversimplified what's needed for this type of change. I am also aware that I may not know what exists to solve all the above. However, if I don't know it, then there are a ton of other people who also don't know it and this is where we need to start. We need to understand how we will tactically achieve this "better" world.

What are we fixing?

I've had conversations on metadata-driven automation, and like atomic modeling, I am not clear on who we are helping and how. What are we improving and in what timeframe? In the end, it feels like we have optimized for something only a few companies can do. To do anchor modeling well would be a huge expense, and when things go wrong, there are several points of failure. When we look at business problems, we need to be sure to optimize the end-to-end system. We can't locally optimize one area because we are likely moving the problem somewhere else. This can be in terms of time, money, or usability.

Decision-makers are not interested in data modeling. They are expecting results and a faster time to market. It's hard enough getting people to do things "better." This is why I find it hard to imagine that we can get to this level of maturity any time soon.

What can we do instead?

There are incremental steps we can take to incorporate best practices into the modern data stack. We need to help people mature their data practice faster, and we should not let perfection get in the way of good. Most companies are not large enterprises with millions of dollars to spend on initiatives like atomic modeling. That being said, I have yet to see anchor modeling in practice, so I welcome the opportunity to learn. I remember years ago the debates about how Ruby on Rials was teaching people "bad practices."  The other side of that argument is that Rails helped companies like Twitter and Github launch businesses faster. Rails was also better than the alternative at the time, which included messy PHP code. Others advocated for well-crafted "scalable" and expensive Java applications. Rails may not be the powerhouse it once was, but it has had a huge influence on how we build software. I even see its influence in dbt even if it might not have been intentional or direct.

Conclusion

Tools like Snowflake and dbt allow us to build processes that are much better than what most people have. Should we focus on all the "bad" things that may come with the modern data stack? Should we focus on how practitioners are not well educated, and so we need to throw all they are doing out?

I don't think so; I believe that we can help companies mature their data practices faster. Will we have the best data models? Maybe not. Will users do things perfectly? Nope. But can we help them move faster and guide them along their journey to avoid big pitfalls? I think we can. Getting people to use git, automating testing, and creating DataOps processes is a huge step forward for many organizations. Let's start there.

There's a reason Data Mesh and the Modern Data Stack resonate with so many people. There's a desire to do things faster with more autonomy at many companies, not just the ones with multi-million-dollar budgets. Let's focus on what is achievable, do the best we can, and help people mature along the way. We don't need more complexity; we need less.

In continuation of our previous blog discussing the importance of implementing DataOps, we now turn our attention to the tools that can efficiently streamline your processes. Additionally, we will explore real-life examples of successful implementations, illustrating the tangible benefits of adopting DataOps practices.

Which DataOps tools can help streamline your processes?

There are a lot of DataOps tools that can help you automate data processes, manage data pipelines, and ensure the quality of your data. These tools can help data teams work faster, make fewer mistakes, and deliver data products more quickly.

Here are some recommended tools needed for a robust DataOps process:

1. dbt (data build tool): dbt is an open-source data transformation tool that lets teams use SQL to change the data in their warehouse. dbt has a lightweight modeling layer and features like dependency management, testing, and the ability to create documentation. Since dbt uses version-controlled code, it is easy to see changes to data transformations (code reviews) before they are put into production.  dbt can dynamically change the target of a query's "FROM" statement on the fly and this allows us to run the same code against development, test, and production databases by just changing a configuration. During the CI process, dbt also lets us run only the changed transformations and their downstream dependencies.
2. Fivetran: Fivetran is an Extract and Load(EL) tool that has been gaining in popularity in recent years since it removes the complexity of writing and maintaining custom scripts to extract data from SaaS tools like Salesforce and Google Analytics.  By automating extracting data from hundred's of sources Fivetran removes complexity freeing data engineers to work on projects with a bigger impact. Finally, Fivetran has a robust API which allows you to save configurations done vie their UI for disaster recovery or to promote configurations form a development to a production environment.
3. Airbyte: Airbyte is another data-ingestion EL tool that is appealing because it is open source, requires little or no code, and is run by the community. It also makes it easier to extract and load data without having to do custom coding.  Airbyte also offers a connector development kit to help companies build custom connectors that may not be available. This allows companies to leverage most of the Airbyte functionality without too much work. There's also an API that can be used to retrieve configurations for disaster recovery.
4. SQLFluff: SQLFluff is an open-source SQL linter that helps teams make sure their SQL code is consistent and follows best practices. It gives you a set of rules that you can change to find and fix syntax errors, inconsistencies in style, and other common mistakes. Sqlfluff can be added to the CI/CD pipeline so that problems are automatically found before they are added to the codebase. By using a tool like SQLFluff, you can make sure your team follows a consistant coding style and this will help with long term project maintainability.
5. dbt-checkpoint: dbt-checkpoint provides validators to make sure your dbt projects are good. dbt is great, but when a project has a lot of models, sources, and macros, it gets hard for all the data analysts and analytics engineers to maintain the same level of quality. Users forget to update the columns in property (yml) files or add descriptions for the table and columns. Without automation, reviewers have to do more work and may miss mistakes that weren't made on purpose. Organizations can add automated validations with dbt-checkpoint, which makes the code review and release process better.
6. Hashboard: Hashboard is a business intelligence (BI) product built for data engineers to do their best work and easily spread the data love to their entire organizations. Hashboard has an interactive data exploration tool that enables anyone in an organization to discover actionable insights.
7. GitHub: GitHub offers a cloud-based Git repository hosting service. It makes it easier for people and teams to use Git for version control and to work together. GitHub can also run the workflows needed for CI/CD and it provides a simple UI for teams to perform code reviews and allows for approvals before code is moved to production.
8. Docker: Docker makes it easy for data teams to manage dependencies such as the versions of libraries such as dbt, dbt-checkpoint, SQLFluff, etc.. Docker makes development workflows more robust by integrating the development pipeline and combining dependencies simplifying reproducibility.

Examples of companies who have successfully implemented DataOps

DataOps has been successfully used in the real world by companies of all sizes, from small startups to large corporations. The DataOps methodology is based on collaboration, automation, and monitoring throughout the entire data lifecycle, from collecting data to using it. Organizations can get insights faster, be more productive, and improve the quality of their data. DataOps has been used successfully in many industries, including finance, healthcare, retail, and technology.

Here are a few examples of real-world organizations that have used DataOps well:

1. Optum: Optum is part of UnitedHealthcare. Optum prioritizes healthcare data management and analytics and when they wanted to implement new features and apps quickly, they turned to DataOps. DataOps helped Optum break down silos, saving millions of dollars annually by reducing compute usage. Optum managed data from dozens of sources via thousands of APIs, which was its biggest challenge. A massive standardization and modernization effort created a scalable, centralized data platform that seamlessly shared information across multiple consumers.
2. JetBlue: DataOps helped JetBlue make data-driven decisions. After struggling with an on-premises data warehouse, the airline migrated to the cloud to enable self-service reporting and machine learning. They've cleaned, organized, and standardized their data and leveraged DataOps to create robust processes. Their agility in data curation has enabled them to increase data science initiatives.
3. HubSpot: HubSpot is a leading company that makes software for inbound marketing and sales. It used DataOps to improve the use of its data. By using a DataOps approach, HubSpot was empowered to do data modeling the right way, to define model dependencies, and to update and troubleshoot models, which resulted in a highly scalable database and opened up new data application possibilities.
4. Nasdaq: Nasdaq, a global technology company that provides trading, clearing, and exchange technology, adopted DataOps to improve its data processing and analysis capabilities. They launched a data warehouse, products, and marketplaces quickly. After scalability issues, they moved to a data lake and optimized their data infrastructure 6x. The migration reduced maintenance costs and enabled analytics, ETL, reporting, and data visualization. This enabled better and faster business opportunity analysis.  
5. Monzo: Monzo is a UK-based digital bank that used DataOps to create a data-driven culture and improve its customer experience. By letting everyone make and look at the different data maps, they are helping teams figure out how their changes affect the different levels of their data warehouse. This gave the Monzo data team confidence that the data they give to end users is correct.

What is the future of DataOps adoption?

DataOps has a bright future because more and more businesses are realizing how important data is to their success. With the exponential growth of data, it is becoming more and more important for organizations to manage it well. DataOps will likely be used by more and more companies as they try to streamline their data management processes and cut costs. Cloud-based data management platforms have made it easier for organizations to manage their data well. Some of the main benefits of these platforms are that they are scalable, flexible, and cost-effective. With DataOps teams can improve collaboration, agility, and build trust in data by creating processes that test changes before they are rolled out to production.

With the development of modern data tools, companies can now adopt software development best practices in analytics. In today’s fast-paced world, it's important to give teams the tools they need to respond quickly to changes in the market by using high-quality data. Companies should use DataOps if they want to manage data better and reduct the technical debt created from uncontrolled processes. Putting DataOps processes in place for the first time can be hard, and it's easier said than done. DataOps requires a change in attitude, a willingness to try out new technologies and ways of doing things, and a commitment to continuous improvement. If an organization is serious about using DataOps, it must invest in the training, infrastructure, and cultural changes that are needed to make it work. With the right approach, companies can get the most out of DataOps and help their businesses deliver better outcomes.

At Datacoves, we offer a suite of DataOps tools to help organizations implement DataOps quickly and efficiently. We enable organizations to start automating simple processes and gradually build out more complex ones as their needs evolve. Our team has extensive experience guiding organizations through the DataOps implementation process.

Schedule a call with us, and we'll explain how dbt and DataOps can help you mature your data processes.