How a Separate Data Transformation Tool Accelerates Your Analytics

  • John Morrell
  • December 21, 2021

Data engineers and analysts have a multitude of options as to how they can transform their data.  They could:

  • Extract, Transform, then Load (ETL)
  • Extract, Load, then Transform (ELT)
  • Extract, Transform, Load, then Transform more (ETLT)

This then leads to the next question – where do I transform my data?  With the ETL approach, you are likely to transform data within your data integration tool.  With the ELT or ETLT approach, you have the option of:

  • Transforming data with your ELT data integration tool, if it has transformation capabilities,
  • Writing Python and SQL code, or
  • Using a specialized data transformation tool

The one advantage of transforming data within your ETL or ELT data integration tool is that you can define, manage, and execute end-to-end data pipelines.  This provides a central place to manage all aspects of your data pipelines.

But as organizations are building modern data stacks with cloud data warehouses such as Snowflake at the heart, many choose to separate out their data transformation, either using Python/SQL coding, or using specialized data transformation tools.  There are also orchestration tools such as Airflow that allow you manage and execute entire data pipelines across your EL and T tools.

Let’s look at why using a specialized data transformation tool is the right answer.

Data Integration Tools are Complex

Data engineers are the authors of your data pipelines.  Data integration tools are designed to let data engineers get to any level of sophistication in their data pipelines as needed.

With this sophistication comes complexity.  The transformation aspect of data integration tools is often complex, requiring a great deal of intermediate schema definition and use of SQL.   The low-code transformation capabilities of ETL tools are limited.  The types of transformations are also limited.

Data transformation within data integration tools is definitely not suitable for an everyday analyst.  This complexity can also cause data engineers to create errors or wrongly code transformations and models.

Getting the Entire Team Involved

Data engineers tend to know more about the data itself – where it resides, how it is structured and formatted, and how to get it – and less about how the business uses the data. Their role is highly focused on managing data.

Data analysts know less about the structure of the data but have a complete understanding of how the business would use the data and how it would be incorporated into analytics. They may have varying technical skills but would prefer to spend more time on what they are good at, modeling data and analyzing it, and less on coding data transformation.

Successful, agile modern analytics requires the ability to get the broader data analytics team involved in data modeling and transformation – not just relying on the data engineering teams.  Modern data modeling and transformation tools embrace bringing the less coding-centric analytics team to model and transform their data.

In such a model, data engineers can focus on managing and moving the data as needed into the cloud data warehouse.  Analysts can then take the data and transform it to their specific needs.  New analyses are performed faster.  And each group can use their highly tuned knowledge and skills as outlined above.


Data integration tools have limited facilities for collaboration.  And, if your analyst community has a difficult time using or doesn’t use your data integration tools, they are completely shut out of a collaboration process.

Modern data transformation tools provide a central place where the data and analytics teams can share models and have collaborative processes around data modeling. This makes the entire team far more efficient and productive to deliver projects in an agile manner.

Beyond making the process efficient and increasing team productivity, a collaborative data transformation workflow eliminates manual handoffs and misinterpretation of requirements. This adds one more highly valuable benefit: it eliminates errors in the data transformations and ensures models get done right the first time.

Searchable Catalog and Data Documentation

As we discussed earlier, knowledge about data is spread over multiple groups: data engineers and analytics teams.  Business teams may have their own unique knowledge of data and how they use it.  In addition, there is a large variety of information about data captured, both automated, machine-driven and user supplied.

Modern data transformation tools help you capture all this information about your data.  Good data transformation tools with go beyond simple data documentation and capture a great deal of information about the data, including:

  • Auto-generated documentation and information such as schema information, transformations performed, data lineage, audits, and certain system-generated properties.
  • User-supplied knowledge such as comments, descriptions, tags, custom properties, and business metadata.

Assets, and the information about them, are only valuable if the broader community of analysts and data scientists can find these assets.  Your data transformation tool needs to supply a rich faceted search capability, allowing users to easily search and drill down to potential assets they could use. The search indexes all of the available information discussed above and provides facets users can drill into, such as system and custom properties, tags, and more.

Separate your T

Use your EL tool for what it is good for – extracting and loading your data into your cloud data warehouse.  Modern EL tools are excellent at that.  They help abstract the complex APIs in front of your applications to make it easy to get the data out of those applications.  But they are not good at data transformation.

So, the next question around separating your T is – why don’t I just write Python and SQL?  By just writing code, you leave out a portion of the analyst community that is less skilled in these areas.  In general, coding takes up to 10 times longer than no-code approaches.  Coding also eliminates the ability for team collaboration and creating a rich, searchable data catalog and documentation.

Modern data transformation tools such as Datameer provide an agile environment for data modeling and transformation that allows you to answer new analytics questions in minutes, not days or weeks, make your teams more efficient, and lower your data engineering costs.  Datameer does this through:

  • A unique combined code/no-code toolset that allows anyone in your organization model and transform data to get your broader team involved,
  • A highly communicative and collaborative workflow across the entire team of data engineers and the analytics community, and
  • A rich, searchable catalog-like set of data documentation, tagging, commenting, custom properties, business metadata that facilitates easy knowledge sharing and discovery.

Datameer gets the entire team working together, communicating, and sharing the workload. This enables faster, more agile data transformation processes and eliminates errors due to misinterpretation to ensure data transformation models get done right the first time.

Are you interested in seeing Datameer in action. Sign up for your free trial today!

Related Posts

Top 5 Snowflake tools for Analysts- talend

Top 5 Snowflake Tools for Analysts

  • Ndz Anthony
  • February 26, 2024
Must Have Data Analysis Tools in 2022

Must Have Data Analysis Tools in 2024

  • Datameer, Inc.
  • February 4, 2024