Replicate SQL Server data to Amazon S3.

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Load SQL Server data to Amazon S3

The easiest and fastest way to replicate SQL Server data to Amazon S3

Need to replicate SQL Server data to Amazon S3? Debating about which SQL Server replication tool to use? There are many automated data replication tools out there that claim to ETL your SQL Server data to Amazon S3 quickly. However there are certain points you may have overlooked and should definitely be considered.

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Why migrate SQL Server data to S3 with BryteFlow

  • Low impact, low latency transactional log replication from SQL Server to Amazon S3.
  • BryteFlow automatically optimises S3 with columnar file formats, partitioning and compression.
  • BryteFlow integrates seamlessly with AWS Athena, Lake Formation, Glue Data Catalog and Redshift Spectrum to make them run 15x faster.
  • Save on data integration costs by 25 times*.

*Results based on trials conducted at a client site

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Real-time, codeless, automated SQL Server data replication to Amazon S3

Can your replication tool replicate really, really large volumes of SQL Server data to your Amazon S3 data lake fast?

When your data tables are true Godzillas, including SQL Server data, most data replication software roll over and die. Not BryteFlow. It tackles terabytes of data for SQL Server replication head-on. BryteFlow XL Ingest has been specially created to replicate huge SQL data to Amazon S3 at super-fast speeds.
Try BryteFlow free and see the difference.

Access Operational Metadata out of the box

BryteFlow keeps operational metadata out of the box of all the extraction and load processes. This can be saved on Aurora if required. The metadata includes currency of data and data lineage. Currency of data shows the status of the data whether it is active, archived, or purged. Data lineage represents the history of the migrated data and transformation applied on it.

Prepare data on Amazon S3 and copy to Amazon Redshift or use Redshift Spectrum to query data on Amazon S3

BryteFlow provides the option of preparing data on S3 and to copy it to Redshift for complex querying. Or you can use S3 Spectrum to query the data on S3 without actually loading it onto Amazon Redshift. This distributes the data processing load over S3 and Redshift saving hugely on processing and storage cost and time.

How much time do your Database Administrators need to spend on managing the replication?

You need to work out how much time your DBAs will need to spend on the solution, in managing backups, managing dependencies until the changes have been processed, in configuring full backups and then work out the true Total Cost of Ownership (TCO) of the solution. The replication user in most of these replication scenarios needs to have the highest sysadmin privileges.

With BryteFlow, it is “set and forget”. There is no involvement from the DBAs required on a continual basis, hence the TCO is much lower. Further, you do not need sysadmin privileges for the replication user.

Are you sure SQL Server replication to Amazon S3 and transformation are completely automated?

This is a big one. Most SQL Server data tools will set up connectors and pipelines to stream your SQL Server data to S3 but there is usually coding involved at some point for e.g. to merge data for basic SQL Server CDC. With BryteFlow you never face any of those annoyances. SQL Server data replication, data merges, SCD Type2 history, data transformation and data reconciliation are all automated and self-service with a point and click interface that ordinary business users can use with ease.

Is your data from SQL Server to Amazon S3 monitored for data completeness from start to finish?

BryteFlow provides end-to-end monitoring of data. Reliability is our strong focus as the success of the analytics projects depends on this reliability. Unlike other software which set up connectors and pipelines to SQL Server source applications and stream your data without checking the data accuracy or completeness, BryteFlow makes it a point to track your data. For e.g. if you are replicating SQL Server data to S3 at 2pm on Thursday, Nov. 2019, all the changes that happened till that point will be replicated to the S3 database, latest change last so the data will be replicated with all inserts, deletes and changes present at source at that point in time.

Does your data integration software use time-consuming ETL or efficient SQL Server CDC to replicate changes?

Very often software depends on a full refresh to update destination data with changes at source. This is time consuming and affects source systems negatively, impacting productivity and performance. BryteFlow uses SQL Server CDC to S3 which is zero impact and uses database transaction logs to query SQL Server data at source and copies only the changes into the Amazon S3 database. The data in the S3 data lake is updated in real-time or at a frequency of your choice. Log based SQL Server CDC is absolutely the fastest, most efficient way to replicate your SQL Server data to the Amazon S3 data lake.

Does your data maintain Referential Integrity?

With BryteFlow you can maintain the referential integrity of your data when replicating SQL Server data to AWS S3. What does this mean? Simply put, it means when there are changes in the SQL Server source and when those changes are replicated to the destination (S3) you can put your finger exactly on the date, the time and the values that changed at the columnar level.

Is your data continually reconciled in the S3 data lake?

With BryteFlow, data in the S3 data lake is validated against data in the SQL Server replication database continually or you can choose a frequency for this to happen. It performs point-in-time data completeness checks for complete datasets including type-2. It compares row counts and columns checksum in the SQL Server replication database and S3 data at a very granular level.Very few data integration software provide this feature.

Do you have the option to archive data while preserving SCD Type 2 history?

BryteFlow does. It provides time-stamped data and the versioning feature allows you to retrieve data from any point on the timeline. This versioning feature is a ‘must have’ for historical and predictive trend analysis.

Can your data get automatic catch-up from network dropout?

If there is a power outage or network failure will you need to start the SQL Server data replication to S3 process over again? Yes, with most software but not with BryteFlow. You can simply pick up where you left off – automatically.

Can your SQL Server data be merged with data from other sources?

With BryteFlow you can merge any kind of data from multiple sources with your data from SQL Server for Analytics or Machine Learning.
More on BryteFlow’s Data Integration for S3

Load data fast with smart partitioning and compression

BryteFlow Ingest provides parallel sync at the initial ingest of data and compresses and partitions data so it can be loaded extremely fast. This has minimal impact on source and the SQL Server data replication proceeds smoothly. Even in the case that your data replication is interrupted by a network outage, your data replication just starts from the last partition that was being ingested instead of the beginning.

Since BryteFlow Ingest compresses and stores data on Amazon S3 in smart partitions you can run queries very fast even with many other users running queries concurrently. It eliminates heavy batch processing, so your users can access current data, even from heavy loaded EDWs or Transactional Systems.

BryteFlow interfaces seamlessly with AWS Lake Formation and Glue Data Catalog for optimal functioning

BryteFlow interfaces seamlessly with AWS Lake Formation and adds automation to the mix so you can deploy an S3 data lake 10x faster while taking advantage of everything AWS Lake Formation has to offer, including finer grain access control.

BryteFlow also interfaces directly with the Glue Data Catalog via API. Information in the Glue Data Catalog is stored as metadata tables and helps with ETL processing. BryteFlow enables automated partitioning of tables and automated populating of the Glue Data Catalog with metadata so you can bypass laborious coding and extract and query data faster.

BryteFlow’s Technical Architecture

BryteFlow’s Data Integration Tools

The BryteFlow software consists of data integration tools that work synergistically to deliver flawlessly
replicated, prepared data that you can use for your Analytics, ML, AI or other applications.
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BryteFlow Ingest: our data replication tool

The data replication superstar, replicates data from any file, database or API.
About BryteFlow Ingest 

BryteFlow XL Ingest for data replication of very large datasets

Specially designed to replicate tables over 50 GB fast and seamlessly.
About BryteFlow XL Ingest

BryteFlow Blend: our data transformation tool

Merges data from different sources and prepares it for Analytics, Machine Learning etc.
About BryteFlow Blend

BryteFlow TruData: our data reconciliation tool

Ensures completeness of data including Type2, issues alerts if data is missing.
About BryteFlow TruData

BryteFlow ControlRoom: our data monitoring tool

The Bryteflow ControlRoom is an operational dashboard that monitors all instances of BryteFlow Ingest and BryteFlow Blend, displaying the statuses of various replication and transform instances.
About BryteFlow ControlRoom

About Microsoft SQL Server

Microsoft SQL Server is a software that is a relational database management system owned by Microsoft. It’s primary objective is to store data and then retrieve it when other applications request it. It supports a huge range of applications including transaction processing, analytics and business intelligence. The SQL Server is a database server that implements SQL (Structured Query Language) and there are many versions of SQL Server, engineered for different workloads and demands.

About Amazon S3

Amazon S3 or Amazon Simple Storage Service is an object storage service that is scalable, flexible, always available and highly secure. It can be used by all kinds of industries to store petabytes of data. Data on S3 is stored in S3 buckets and can be used in many applications including websites, mobile apps, IoT devices, enterprise applications and big data analytics. Companies can build highly durable data lakes on Amazon S3 and organize data as per requirement for storage or analytics.