Migrate Netezza data to Snowflake
Why you should use BryteFlow to move your Netezza data to Snowflake
Netezza is a pioneer in the data warehousing field and is an IBM product. It uses Massively Parallel Processing to deliver data. Recently however, cloud data warehouses like Snowflake are proving to be more wallet-friendly for organizations, offering infinite scalability, managed services, ease of use and much lower costs. Build a Snowflake Data Lake or Snowflake Data Warehouse
Migrate huge datasets from Netezza to Snowflake without a hassle, super-fast
Large scale data migration from Netezza to Snowflake comes with its own set of challenges – 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 Netezza migration to Snowflake -no coding, no delays and very cost-effective. Get ETL in Snowflake
Real-time, codeless, automated Netezza data migration to Snowflake
Automated creation of tables on Snowflake, no coding
BryteFlow migrates your data model from Netezza automatically. The data model model includes the tables, databases, views, sequences, user account names, roles and objects grants etc. BryteFlow Ingest creates your tables on Snowflake automatically so you never need to code.
Change Data Capture Types and CDC Automation
Smart partitioning and parallel sync to load data
If you have petabytes of data to migrate from Netezza 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 Netezza 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 Netezza 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 enables configuration of custom business logic to collect data from multiple applications or modules into AI and Machine Learning ready inputs.
Automatic catch-up from network dropout
If there is a power outage or network failure you don’t need to worry about starting the Netezza data migration to Snowflake process over again. BryteFlow Ingest will automatically resume from where it left off, saving you hours of precious time.
Netezza was the world’s first data warehouse appliance and created in 2003. It delivered a great performance because of its patented hardware acceleration process that featured field-programmable gate arrays (FPGA). Netezza is owned by IBM which later withdrew its support due to the Cloud data revolution. Data in the cloud is a much more attractive proposition to organizations, providing infinite scalability, faster deployment, increased reliability and a pay-as-you-go model that lowers IT costs.
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.