5 ways we can help your successful migration to the Snowflake Data Cloud

The enterprise data warehouse is a key target for cloud migration.

For many companies, the Snowflake Data Cloud is becoming the platform of choice for their modern data warehouse offering a number of potential benefits including:

  • Flexible performance: Snowflake decouples storage and compute functions allowing organisations to scale the warehouse up or down depending on the load
  • Support for both structured and unstructured data: Snowflake allows structured and unstructured data to be combined and loaded into the cloud data warehouse without having to perform conversions, and automatically optimises how each dataset is stored and queried.
  • Accessibility: Snowflake is available within the Amzon cloud and is ditributed across multipler availability zomes to ensure access and availability.
The enterprise data warehouse is a key target for cloud migration.

At the same time, shifting the enterpsie data warehouse to the cloud does present some challenges and opportunities.

Through. our various technology solutions, we can assist you to make this shift as painless as possible, while unlocking the opportunities to optimise and enhance analytics, democratise data and deliver w world-class enterprise data warehouse

Untangling your legacy architecture

Shifting an existing data warehouse to the cloud requires an understanding of existing data connections, flows and transformations.

This understanding is necessary both to ensure that existing, required reporting capabilities are replicated in the new environment; and to provide an opportunity to re-engineer and optimise the new Snowflake warehouse during the migration process.

Most enterprise data warehouses have intricate business logic woven into stored procedures – aggregations, calculations and the like – that in many cases is not be well documented or understood.

More broadly than the warehouse itself, data flows may combine ETL processes and code (JAVA, Python, SQL etc) that manipulate data as it is moved to the warehouse. Again, this logic may be poorly documented and understood.

Our MANTA Unified Lineage Platform automatically reads the code driving your legacy enterprise data warehouse to provide you with a visual representation of data flows and transformations.

MANTA makes it easy to understand what you have, and what you need to replicate or optimise as you move to Snowflake. Data sufficiency analysis makes sure that every column in the existing data warehouse maps to a similar column in the planned structures and reduces the risk that something will be missed in translation.

Automated Snowflake lineage

At the same time, MANTA automatically ingests Snowflake data flows and transformations to document and display your new warehouse lineage.

MANTA’s Snowflake scanner generates detailed lineage for a comprehensive overview of the journey the data undergoes throughout its lifecycle. By leveraging MANTA’s scanner to map lineage in Snowflake, users can understand their data in context, increase business agility without sacrificing quality, and deliver data intelligence that fuels growth. 

MANTA can connect to the Snowflake database to automatically read Snowflake metadata and output a detailed lineage map to help users understand where data is flowing from, which transformations happen in Snowflake, and how everything is impacted downstream. 

Lineage makes it easier to streamline the Snowflake data pipelines to distil environment complexity and empower collaboration and self-service.

Automated lineage also builds trust in the new warehouse, and associated reports and analyses, by providing confidence in the source and veracity of data.

Eliminating manual processes is a key enabler for the DataOps approach to analytics to provide faster time to trusted insights.

Moving legacy data siloes to Snowflake

Snowflake has garnered attention for its architecture which allows for the separation of storage and compute which allows each piece to scale individually. In turn, it helps people share data in real-time. Despite Snowflake’s unique architecture, you might find that Snowflake cannot readily understand complex data from legacy sources. How can you solve this problem?

Precisely Connect removes Snowflake data gaps through data integration that understands complex data like VSAM, Db2 or IMS. With Connect, companies have confidence that the data feeding their analytics are complete.

Because Connect is efficient and has a small footprint, you don’t have to worry about performance loss. Additionally, they support the management, security, and governance of modern data architectures, so you have peace of mind that this solution won’t compromise any existing processes. 

Data quality and enrichment

Snowflake users can easily connect our Precisely data quality tools to profile and cleanse Snowflake data. Where gaps in data are discovered, users can search for and access Precisely data sets – including location, demographics and similar contextual information – directly from the Snowflake Data marketplace.

Privacy by Design

Privacy concerns can block efforts to move analytics to the Cloud. While Snowflake provides various security features it does not provide the granular level of access control necessary to protect sensitive and PII data whilst enabling broad access to analytics datasets.

Okera provides dynamic, row-level security and fine-grained access control to simplify data access control for Snowflake.

In plain English – Okera allows you to provide access to datasets secure in the knowledge that individual fields and rows will be restricted based on the individual accessing them.

Okera’s security features maintain the integrity of the underlying data – for example maintaining uniqueness indicators or shapes of data elements – and are applied dynamically with minimal impact on performance and scalability.

Okera’s support for Snowflake includes:
  • Snowflake native SQL syntax -Okera provides complete end-to-end support for the Snowflake native SQL syntax, making the full power of Snowflake Data Cloud available for all user queries.
  • Snowflake Worksheets: With Okera’s data authorization capabilities, data governance teams can more quickly and confidently grant power users the privilege of running native Snowflake queries directly in the Snowflake web interface.
  • Snowflake Connectors: Combined with support for Snowflake’s native SQL syntax, Okera works with native JDBC/ODBC connectors for the Snowflake Data Cloud. Native connectivity protects investments in business dashboards, reports, and advanced analytics built using third-party BI and other SQL tools.
  • Snowflake  UDFs: Okera uses User Defined Functions (UDFs) to implement data security and privacy transformations, such as dynamic data masking and tokenization. Joint customers can also take advantage of the complete Java programming language to create their own UDFs for custom data security use cases and increase speed at run-time.
  • Snowflake  Access History API: Okera can use Snowflake’s Access History API to incorporate Snowflake’s native audit capability into the Okera platform. Okera enables the ability to look across data residing within Snowflake and other data platforms, providing additional security context for Snowflake data usage, including what tags are being used and by whom.

Catalog and Share

Without visibility, the best data warehouse in the world can have only limited value. Our Precisely Data360 Catalog makes it easy to harvest, publish and share Snowflake dataset definitions, bring business context and governance, and document and share related reports.

Users can more easily find the Snowflake data that they need to do their jobs, and have visibility on which reports and analytics models are already available.

Data sharing agreements and workflows make data more easily accessible, without sacrificing governance.

The data catalogue is a cornerstone of DataOps – ensuring collaboration, speeding impact assessments, and enabling data democratisation.

Build a world-class Snowflake Data Warehouse

Moving to Snowflake presents more than just an opportunity to optimise infrastructure. It presents an opportunity to make data more accessible and useful to a broad group of stakeholders.

Will you take this opportunity?

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