Easy, fast data transformation for analytics-ready data.


Data Transformation

What is Data Transformation?

For the purposes of data integration you need to replicate data from various sources, merge it and change the data into a format that is suitable for use on the destination database or system. This process of changing the format or structure of the data is data transformation. Data transformation involves multiple activities including data discovery, cleansing data, data mapping, aggregating and converting data formats etc.

Types of Data Transformation

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.

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.

ELT or Extract Load Transform

ELT or 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 Data Warehouse for the heavy lifting. BryteFlow’s ELT in Snowflake

With the ever increasing nature of data, this alternate method is proving to be a bottleneck as well, as the transformation or data integration workloads compete with the business user queries on the Data Warehouse, and querying times increase, leading to unhappy users. The only option is to increase the spend on the Data Warehouse by deploying more compute and storage which is very expensive.

The BryteFlow Approach: Modern Data Integration on the Cloud

BryteFlow uses a radically different approach to data transformation we like to call Modern Data Integration. This uses a unique distributed architecture for transforming or preparing data on the cloud. This architecture leverages Amazon S3 to provide a seamless, fast data ingestion and preparation experience. It uses the object storage namely Amazon S3 as the storage layer and various AWS cloud services to orchestrate the data integration and then saves the data back to the object storage.

Frees up the processing power of your Data Warehouse

The data is now available in the raw form and as curated data assets for Data Analytics and Data Science uses cases, and also for any Data Warehouse including Amazon Redshift and Snowflake. The compiled or curated data assets can 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 Blend is our data transformation tool that transforms, remodels, schedules and merges data from multiple sources in real-time. BryteFlow Blend lets you blend and merge virtually any data to prepare data models for Analytics, AI and ML.

Take a first hand look at our data transformation approach. Get in touch with us for a FREE Trial.

BryteFlow uses Amazon S3 as an awesome data transformation platform.

Amazon S3 has infinite scalability

Got some heavy data transformations to do? Amazon S3 is infinitely scalable for data storage. BryteFlow spins up  compute capabilities when required by recruiting additional Amazon EMR clusters for scalability and concurrency so the system never slows down and users never feel the pinch.

Powerful and flexible data integration

Data can be consumed raw or modelled. You get seamless integration of data between multiple compute platforms including various AWS services supporting Relational DW, Hadoop, Machine Learning and Artificial Intelligence.

Lower data costs

Storage and Compute are separate so you only pay when you process data. Storage on Amazon S3 is cheap at a maximum of 3 cents per GB so you can store all of your data unlike a much more expensive data warehouse.

BryteFlow Blend

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.

With just a few clicks, you can either process / transform data in Amazon EMR using Bryte’s intuitive SQL on Amazon S3 user interface or load the data to Amazon Redshift, Snowflake or other destinations.

Read more about data transformation with BryteFlow Blend.

  • Completely codeless and automated 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 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.
  • Full metadata and data lineage.
  • Automatic catch-up from network dropout.