Solving the Data Transformation Bottleneck
- October 21, 2021
Data transformation is the biggest bottleneck in the analytics workflow. To solve the data transformation bottleneck we can look at the modern approach to data pipelines is ELT, or extract, transform, and load, with data transformation performed in your Snowflake data warehouse. A new breed of “no-/low-code” data transformation tools, such as Datameer, are emerging to allow the wider analytics community to transform data on their own, eliminating analytics bottlenecks. This post was originally published on KDnuggets
Data transformation is integral to the analytics workflow and process. With analytics data coming from an ever growing array of disparate data sources, data transformation models the data to make it more understandable and consumable by the analytics and business teams.
However, data transformation is the biggest bottleneck in the analytics workflow. According to IDC, analytics teams only spend 45% of their time performing analysis, with the remaining time spent searching for and preparing data. Additionally, a survey by TDWI cites a “lack of skilled personnel to model data” (36% of respondents) as the top challenge in cloud data integration.
The modern approach to data pipelines is ELT, or extract, transform, and load, with data transformation performed in your Snowflake data warehouse. But this requires extensive SQL and Python skills typical with a data engineer and less common in an analytics team. A new breed of “no-/low-code” data transformation tools are emerging to allow the wider analytics community to transform data on their own, eliminating analytics bottlenecks.
Data transformation typically requires two aspects: (a) data modeling and putting together the flow of data transformations, and (b) optimizing how your data transformations work inside Snowflake.
Modeling and Transforming
The first step in data transformation is making the data useful and consumable by your analytics teams. With ELT, raw data is loaded into Snowflake, but each dataset is cryptic, may be dirty, have only basic values, and is not linked with other datasets. Modeling and transforming it for analytics involves data cleansing, canonical modeling into subject-specific datasets, and use case data modeling.
In How to Transform Your Data in Snowflake: Part 1, we explore these details of modeling and transforming your data.
Next, you optimize how your data transformations work inside Snowflake. This will help your transformation and analytical queries execute quickly and keep Snowflake costs low. This involves Snowflake-specific features such as virtual data warehouses and materialized views and techniques to reduce query complexity.
In How to Transform Your Data in Snowflake: Part 2, we explore the details of optimizing your data transformation in Snowflake.
Understanding how to transform your data and optimize it in Snowflake is critical for fast queries, reusable models, and keeping costs low. No-/low-code tools such as Datameer allow you to share the data transformation workload across your entire team to eliminate bottlenecks while giving the control data engineers require.