5 posts tagged with "Starlake"

Summary​

Snowflake offers powerful tools for data loading and transformation, so why consider Starlake? What distinguishes Starlake, and why is it important? This article delves into these questions, exploring how Starlake complements and enhances your Snowflake experience. Specifically, this article tackles the challenges of loading files into Snowflake

Although Starlake supports transformation activities, the scope of this article is limited to data loading.

Introduction​

The advent of declarative programming through tools like Ansible and Terraform, has revolutionized infrastructure deployment by allowing developers to achieve intended goals without specifying the order of code execution.

This paradigm shift brings forth benefits such as reduced error rates, significantly shortened development cycles, enhanced code readability, and increased accessibility for developers of all levels.

This is the story of how a small team of developers crafted a platform that goes beyond the boundaries of conventional data engineering by applying a declarative approach to data extraction, loading, transformation and orchestration.

Data exposition strategies​

Data may be exposed using views or authorized views and more recently using Row / Column level security.

Historically, to restrict access on specific columns or rows in BigQuery, one can create a (authorized) view with a SQL request like the one below:

Data Loading strategies​

When loading data into BigQuery, you may want to:

• Overwrite the existing data and replace it with the incoming data.
• Append incoming data to existing
• Dynamic partition Overwrite where only the partitions to which the incoming data belong to are overwritten.
• Merge incoming data with existing data by keeping the newest version of each record.

For performance reasons, when having huge amount of data, tables are usually split into multiple partitions. BigQuery supports range partitioning which are uncommon and date/time partitioning which is the most widely used type of partitioning.

Pipelining fast data is big. Pipelining big data fast is bigger. :)