Snowflake for Data Lakes – Improving Performance and Data Access

Snowflake for Data Lakes
Snowflake for Data Lakes

A common description of a data lake is hard to agree on because of its various uses. Data lakes are primarily a data architecture structure that is created to store high volumes of ingested data for later processing and analysis. While in the past, data marts and data warehouses were taken to be separate entities from data lakes, those lines of distinction are no longer prevalent on a modern cloud data platform.

Data today is not thought about as existing in different systems like data marts, legacy data warehouses, and data lakes. The introduction of Snowflake Data Lake has changed the complete data engineering landscape by eliminating the need for developing, deploying, and maintaining these distinct data storage systems. For the first time, businesses have access to one enterprise-level cloud data platform that allows seamless management of structured and semi-structured data like tables and JSON in an all-inclusive way.

Traditional data repositories have to move data through data zones and businesses often have to spend valuable time figuring out how best to do so with the minimum of effort. But Snowflake data lake has an extensible data architecture that facilitates quick data movement within a specific data cloud environment. Data may be generated via Kafka or another pipeline and persisted into a cloud bucket. From there a transformation mode and engine like Apache Spark transforms the data into a columnar format like Parquet and loads the data into a conformed data zone. The advantage of organizations here is that they do not have to choose between a data lake and a data warehouse any longer.


Improving data access, performance, and security with Snowflake data lake

Snowflake offers highly flexible solutions to enable and improve data lake strategy through a cloud-based architecture, designed to meet specific needs.

There are several advantages of a cloud-based data lake platform like Snowflake.

  • Single point data storage – Huge volumes of structured and semi-structured data like CSV, JSON, ORC, and tables can be easily ingested without the need for separate silos.
  • Flexible computing resources – Dynamic computing resources that change according to the workload and the number of users without affecting running queries or any drop in performance.
  • Flexible storage resources – Snowflake data lake provides flexible and affordable data storage resources. Only the base cost of Snowflake cloud providers like Amazon S3, Google Cloud, and Microsoft Azure is to be paid by businesses.
  • Guaranteed data consistency – Data can be effortlessly manipulated and cross-database links with multi-statement transactions can be done.

Here are some ways that Snowflake can enable your data lake


#Running fast queries on top of the data lake

With Snowflake, you can execute almost an unlimited number of concurrent queries. Multiple users can simultaneously carry out numerous intricate queries without facing any lag or drop in performance.

  • External tables can be used to query data directly in your data lake without moving data
  • Synchronizing external tables with Apache Hive meta-store
  • Using materialized views over external tables to increase the performance and speed of queries
  • Use Snowsight which is the in-built visualization of User Interface for Snowflake to increase data exploration
  • Use partition auto-refresh to register automatically new files from your data lake

#Effortless Data Transformation

With Snowflake Data Lake, virtually all data can be processed and easily transformed back to your data lake by building and running integrated data pipelines.

  • Ensure near-zero maintenance by deploying modern architecture and pipelines for data processing
  • Use ANSI SQL to transform data efficiently
  • Use Snowpipe and Streams & Tasks to auto ingest data and facilitate Change Data Capture (CDC) with continuous data pipelines.
  • Use stored procedures and external functions to extend your pipelines.
  • Create robust data pipelines with different data types and ingestion styles
  • Auto-scale up and down to optimize the performance of the pipeline.


#Why should Snowflake be your Data Lake

Snowflake Data Lake is a cloud-based storage platform that addresses various data lake requirements and multiple use cases across an organization.

  • Bring together your technology landscape on a single platform for a wide range of data workloads. This eliminates the need for maintaining different infrastructures and services.
  • Efficiently compress data before storing it in the data lake
  • Avail fully managed service of Snowflake Data Lake including capacity planning, concurrency, storage allocation, and much more. Focus only on data optimization.
  • Provide a single source of data for all users regardless of location and data workload.
  • Allow data users to access and analyze data in the modern Snowflake Data Lake while ensuring end-to-end encryption, security, and governance.


#Make the most of Governance and heightened data security

Even when data is retained in your existing cloud data lake, Snowflake helps to ensure optimum data governance and security.

  • Precise accessibility due to provision of granular-level access control
  • Strict data masking and external tokenization features enabling working with data loads without revealing confidential information
  • Allowing modern data collaboration with external and internal participants through live and secured data sharing
  • Fine-grain security like column level masking and row-level filtering enabled.

It is because of these inherent benefits that Snowflake Data Lake is so widely accepted by organizations across the world.