Data Transformation in ETL
What is Data Transformation?
Data transformation refers to the process that converts data from one format to another for the purposes of Data Analytics, Machine Learning, AI etc. Data transformation is part of the ETL (Extract transform Load) or ELT (Extract Load Transform) processes. Raw data is extracted from multiple sources including databases, applications, IoT and sensor data and more, to a data repository. This data needs to be cleansed, merged (if required), validated and then converted into a format that is ready for use on the destination, be it a data warehouse or a data lake or lakehouse. BryteFlow Blend, data transformation tool
Data Transformation Processes
Data transformation can include activities like data discovery, cleansing data, data mapping, aggregating and converting data formats etc. Besides these, the data may need to undergo customized data transformations like Filtering (where only specific columns are loaded), Splitting (a column is broken up into multiple columns and vice versa), Joining (combining data from multiple sources), Enriching (defining data structures, formatting of values and semantic layers – e.g. State codes instead of displaying full state names), Deduplication (removal of duplicate data). How to Manage Data Quality (The Case for DQM)
ETL or Extract Transform Load
ETL is a type of data integration that refers to the three steps (Extract, Transform, Load) used to pull data from multiple sources and then transform it into a common data model which is designed for business use cases and performance. It is often used to build a data warehouse. During this process, data is extracted from a source system, transformed into a format that can be analyzed, and loaded into a data warehouse or other system. Data Pipelines & ETL Pipelines
ETL uses the power of the server where the data resides to process data
The ETL type of data integration uses the power of the server where it resides, as the data is extracted and transformed on the ETL server, before being loaded to the Data Warehouse. As the data volumes start growing, the ETL server starts to get bottlenecked and the data cannot be loaded to the Data Warehouse in a timely fashion. Increasing the compute on the ETL server is the only option, but even that cannot cope with the volumes, as architecturally the ETL process is not designed for large volumes. It processes data a row at a time, making data integration slow, non-scalable and cumbersome. Migrating ERP data to the Cloud
ELT or Extract Load Transform
ELT (Extract Load Transform) is an alternate but related approach designed to push processing down to the database for improved performance. Here, the raw data is extracted to the Data Warehouse and then transformed or converted to the common data model using the power of the Data Warehouse. The data is extracted and loaded to the Data Warehouse, and the power of the Data Warehouse is used to transform the data into a common business model. Data is processed with set operations, millions of rows can be transformed in one go, using the compute resources of the Data Warehouse for the heavy lifting. This makes the newer ELT approach the method of choice for many organizations. Data Extraction for ETL simplified
ETL / ELT with BryteFlow
ETL (Extract Transform Load) approach for Data Transformation (on AWS only)
BryteFlow uses the ETL approach with distributed data processing on S3. This means that BryteFlow extracts your multi-source data using Change Data Capture to S3 where it preps and transforms the data using EMR clusters. The transformed curated data can then be loaded to the data warehouse (Snowflake on AWS or Redshift) for querying or even used in the data lake itself to create Machine Learning models. The ETL process enables curated data assets to either be accessed from the object storage or copied to the Data Warehouse, to make business user queries run fast and efficiently. This approach frees up the Data Warehouse, to focus on performance – responding to user queries in seconds while the data transformation is carried out on the cloud storage object. BryteFlow CDC
ELT (Extract Load Transform) approach for Data Transformation
BryteFlow also uses the modern ELT approach to carry out data transformation directly in the data warehouse itself. Here data is extracted from multiple sources, databases like Oracle, SAP, SQL Server, MySQL, Postgres, IoT sensors, CRM applications etc. to the data warehouse – either Snowflake on Azure, Snowflake on AWS, Snowflake on GCP or Redshift on AWS, where it is transformed by BryteFlow Blend to a ready-to-use, consumable format like Parquet, ORC or Avro. BryteFlow Blend has an easy to use drag-and-drop UI and uses simple SQL to carry out data transformation.
Take a closer look at BryteFlow. Contact us for a demo.
Data Transformation in Snowflake on AWS, Azure and GCP Cloud
Data transformation powered by Snowflake
BryteFlow Blend uses the infinite scalbility and compute power of the Snowflake Cloud to power the data transformation, delivering ready-to-use data.
ETL / ELT on Snowflake
Complex, Customized Data Transformations
Besides basic transformation, BryteFlow enables customized data transformations like data splits, joins and merges, and filtering on Snowflake.
How to load terabytes of data to Snowflake fast
Snowflake on Azure, AWS and GCP
BryteFlow Blend transforms data in Snowflake on AWS, Snowflake on Azure and Snowflake on Google Cloud. It has Snowflake best practices and optimization built-in.
Snowflake Data Lake / Data Warehouse
SQL Based Data Transformation
BryteFlow data transformation in Snowflake is SQL-based and low code with a user-friendly, visual drag-and-drop UI. You can easily run all data transformation workflows as an end-to-end ETL process.
SQL Server to Snowflake in 4 easy steps
Smart Partitioning and Compression
BryteFlow uses smart partitioning techniques and compression of data for quick data transformation. Data is transformed in increments on Snowflake, leading to optimal, fast performance.
How to cut down costs by 30% on Snowflake
BryteFlow Blend is our data transformation tool in the BryteFlow suite. BryteFlow Blend lets you remodel, merge, transform any data to prepare data models for Analytics, AI and ML. It uses a proprietary technology that sidesteps laborious PySpark coding to prepare data in real-time with simple SQL.
Read more about data transformation with BryteFlow Blend.
- Low Code data transformation.
- Remodel, transform and merge data from multiple sources in real-time.
- SQL based data management – cut down development time by 90% as compared to coding using PySpark.
- Use the BI tools of your choice to consume data.
- BryteFlow Blend uses smart partitioning techniques and compression of data to deliver super fast performance.
- Create a data-as-a-service environment, where business users can self-serve and encourage data innovation.
- ETL data with full metadata and data lineage.
- Automatic catch-up from network dropout.