SQL based Data Management for Amazon S3 with BryteFlow Blend

BryteFlow Blend is the world’s first full feature SQL for Object Store technology - built & optimized for Amazon S3. It provides a full SQL coding, job scheduling and dependency management workbench for Amazon S3. With BryteFlow blend, Enterprises can leverage cloud data lakes, low cost object stores and modern big data Hadoop or Spark infrastructure using their existing coding skills.

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Data Preparation on your BryteFlow Amazon S3 Data Lake with a self-service point-and-click workbench to select, join and transform data.

Blend automatically converts transformations to powerful and scalable Spark code

Persist Data Assets in Amazon S3 and optionally  export data assets to Amazon Redshift or Amazon Aurora

Automated Amazon KMS Encryption in transit and rest with optional Data Masking

Create a Data-as-a-service environment for your Business users

⦁ Integration with other AWS services for monitoring and alerting

We are proud to be one of the very few companies in the world who are Amazon Advanced Technology Partners with Big Data Competency.

Download Data Sheet - Bryteflow for Blend

As more organisations embrace the cost-effective and scalable AWS S3 object storage as the innovation proving ground for analytics, they are looking to put this limitless power in the hands of analysts. But how will analysts transform data on a powerful Hadoop platform when they are so used to SQL and databases? How do they escape from the tyranny of data preparation and accelerate time-to-analytics?

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BryteFlow Blend

Transform and Prepare Data with Built-in SQL Editor and ETL for Amazon S3

Transform and prepare data using SQL on Amazon S3. Bryte provides an intuitive SQL on the Amazon S3 user interface to build data models without the need to move data into a relational database or data warehouse.

Easily Create and Schedule Amazon S3 Jobs

Choose Amazon S3 Output File Format

  • Easily organize Data into Multiple Amazon S3 folders
  • Manage multiple levels of Data Maturity from Raw Data to Complex Data Marts