Fast Teradata to Snowflake migration without coding
Accelerate database migration from Teradata to Snowflake with BryteFlow
Teradata is an on-premises data warehouse solution that is immensely scalable, supports high concurrency and uses Massively Parallel Processing for delivering data fast. Recently however, cloud data warehouses like Snowflake are proving more cost-effective, separating storage and compute, offering infinite scalability, managed services, ease of use and much lower costs.
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Migrate petabytes of data from Teradata to Snowflake at high speed
Migrating large volumes of data from Teradata to Snowflake is not easy – a huge amount of manual effort and time is needed to transfer data, convert to Snowflake schemas and manage the ongoing replication, while both data warehouses run in parallel. This also involves significant costs. That’s where BryteFlow, with its automated data migration can help. With just a few clicks you can set up your Teradata migration to Snowflake -no coding, no delays and very cost-effective.
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How to load terabytes of data to Snowflake fast
Real-time, automated Teradata data migration to Snowflake
Automated creation of tables on Snowflake, no coding
BryteFlow makes moving your data from Teradata to Snowflake very easy. It migrates your tables and data from Teradata to Snowflake automatically. You just need a couple of clicks to set up the data migration. BryteFlow Ingest creates your tables on Snowflake automatically so you can be up and running fast and never need to code.
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Smart partitioning and parallel sync to load data
If you have petabytes of data to migrate from Teradata to Snowflake, we recommend a initial full ingest with BryteFlow XL Ingest. The data replication tool has been specially created to get across large datasets in minutes. It uses smart partitioning technology to partition the data and parallel sync functionality to load data in parallel threads. Parallel loading threads greatly accelerate the speed of your Teradata data migration to Snowflake. After the initial full ingest, BryteFlow Ingest captures incremental changes so your data at destination is always updated while your data migration takes place.
Data reconciliation to monitor completeness of data
When your data is being migrated from Teradata to Snowflake, you can monitor the completeness of your data with BryteFlow TruData. It is our data reconciliation tool that performs point-in-time data completeness checks for datasets including Type 2 data and provides notifications should data be missing.
Data is ready for analytical, ML and AI consumption
BryteFlow Ingest provides a range of data conversions out of the box including Typecasting and GUID data type conversion to ensure that your data migrated to Snowflake is ready for analytical consumption. Further, BryteFlow automatically merges the data across all deltas with SCD type 2 history if configured. The data is ready to use for Analytics, ML and AI right away.
Automatic catch-up from network dropout
If there is a power outage or network failure you don’t need to worry about starting the Teradata data migration to Snowflake process over again. BryteFlow Ingest will automatically resume from where it left off, saving you hours of precious time.
Teradata is a database solution with Massively Parallel Processing and shared-nothing architecture. It has been around for over 30 years and offers petabyte-scale processing, high scalability and customizability. Teradata is known for performance and has a great feature set that caters to the most exacting of requirements. Traditionally Teradata has been installed on-premises but with a shift to the cloud globally, organizations are considering cloud data warehouses for faster speed and economy.
About Snowflake Data Warehouse
The Snowflake Data Warehouse or Snowflake as it is popularly known is a cloud based data warehouse that is extremely scalable and high performance. It is a SaaS(Software as a Service) solution based on ANSI SQL with a unique architecture. Snowflake’s architecture uses a hybrid of traditional shared-disk and shared-nothing architectures. Users can get to creating tables and start querying them with a minimum of preliminary administration.