BryteFlow Blend for data transformation
BryteFlow Blend is a tool for AWS ETL that transforms, remodels, schedules and merges data on S3 from multiple sources in real-time. Ensure quick time to value – reduce deployment times by 90%.
Your AWS ETL process gets completely automated whether it is real-time data ingestion by BryteFlow Ingest or the data transformation by BryteFlow Blend. BryteFlow Blend is our data transformation tool that lets you blend and merge virtually any data on Amazon S3 in real-time to prepare data models for Analytics, AI and ML.
Our data transformation tool uses a proprietary technology that sidesteps laborious PySpark coding to prepare data with simple SQL. It breaks down your data silos effortlessly (even the stubborn SAP ones). BryteFlow Blend has an intuitive drag and drop interface for data transformation that helps you access analytics-ready data on a variety of platforms in real-time, no coding needed!
- Integrates with BryteFlow Ingest to deliver real-time data from your legacy sources
- Cost-efficient AWS ETL: Pay-as-you-go SQL Based Data Management on Amazon S3 using EMR: run and schedule complex Apache SPARK data transformations by simply using SQL, no PySpark coding required.
- Create a data-as-a-service environment, where business users can self-serve and access analytics-ready data assets.
- Increase productivity and prepare models for Analytics, AI and ML.
- Immediate data validation with BryteFlow TruData, no unpleasant instances of missing data.
- Codeless AWS ETL: Prepare your data on Amazon S3 with BryteFlow Blend and automatically export to Redshift, Snowflake and Aurora if required.
ETL to AWS: Technical Architecture
BryteFlow Blend: automated, codeless data preparation tool for AWS ETL
Remodel, transform and merge data from multiple sources in real-time for AWS ETL.
Remodel, transform, schedule and merge data from multiple sources and break down data silos in real-time or as the raw data is ingested. BryteFlow Blend is ideal for AWS ETL and provides seamless integrations between Amazon S3 and Hadoop on Amazon EMR and MPP Data Warehousing with Amazon Redshift. 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.
SQL based data management – cut down development time by 90% as compared to coding using PySpark.
Run and schedule complex Hadoop/SPARK data transformations by simply using SQL. BryteFlow provides an Enterprise grade Data Preparation workbench on Amazon S3 for ETL on AWS. You can easily create and manage multiple Amazon S3 folders, jobs and dependencies. Categorize data easily into different levels of security classifications and maturity – from raw data through to highly curated data marts.
The point and click interface is very easy to use for AWS ETL.
Run all data preparation and workflows as an end to end process. Select source, destination, and schedule time as per convenience. The job you create is represented by an interactive drag and drop workflow diagram with tasks you can add and connect as you go. This visual representation adds clarity and flexibility to the process. BryteFlow for AWS ETL
Flexibility in consumption of data – use the tools of your choice.
BryteFlow Blend allows you to consume the data with the tools of your choice including Amazon Athena for adhoc queries, favorite visualization tools for dashboards, Redshift Spectrum for joining with data on Redshift, or copy the data assets automatically to Redshift for business intelligence reporting. You can also copy data to Aurora for your web applications or marketing initiatives.
Smart Partitioning and compression for fast, high performance data transformation.
BryteFlow Blend uses smart partitioning techniques and compression of data to deliver super fast performance. Data can be transformed in increments rather than at one go so you get to use your data that much faster.
Create a data-as-a-service environment, where business users can self-serve and encourage data innovation.
The extremely low cost of data storage combined with the separation of compute resources allows your organisation to retain a lot of data. This creates a self-service platform for AWS ETL frees you from the drudgery of data management. Users can create many different processing clusters around the Amazon S3 storage layer using BryteFlow Blend. Each workload operates independently so users can freely interact with data and run the workloads they need.
Integrates with BryteFlow Ingest to run data transformation jobs automatically.
You can configure BryteFlow Blend with BryteFlow Ingest so it will automatically get triggered and get activated when new data is extracted to BryteFlow Ingest. This can save a lot of time for users.
Full metadata and data lineage.
All data assets will have automated metadata and data lineage. This helps in knowing from where your data originated, what data it is and where it is stored.
Automatic catch-up from network dropout.
No need to panic if your data transformation is interrupted by a power outage or a similar situation. You can simply pick up where you left off – automatically. In the event of a system outage or lost connectivity, BryteFlow Blend features an automated catch-up mode so you don’t have to check or start afresh with AWS ETL process.