Remodel, transform and merge data in real-time.


BryteFlow Blend

Transform, remodel, schedule and merge data from multiple sources in real-time with our data preparation tool.

BryteFlow Blend lets you blend and merge virtually any data to prepare data models for Analytics, AI and ML. It 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 click and point interface that lets you access analytics-ready data on a variety of platforms in real time, no coding needed!

Key Highlights:
  • SQL Based Data Management: run and schedule complex Hadoop/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, no unpleasant instances of missing data.
  • BryteFlow Blend makes prepared data and data models available on a large number of destinations.

See how BryteFlow works

BryteFlow Blend: automated, codeless data preparation tool

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Remodel, transform and merge data from multiple sources in real-time.

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 leverages the AWS eco-system 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.

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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. 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.

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The point and click interface is very easy to use.

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.

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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.

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Smart Partitioning and compression for fast, high performance 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.

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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 and an environment that 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.

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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.

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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.

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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.