Data Transformation Tool for ETL and ELT.

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. What to look for in a Real-Time Data Replication Tool

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. Zero-ETL, New Kid on the Block?

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