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Snowflake as a Data Lake

Snowflake, the data warehouse built for the cloud, has numerous use cases across various domains. We can use Snowflake as a Cloud Enterprise Data Warehouse (EDW), as a data lake, in accelerated analytics, in data engineering, in secured data sharing, and data science. This blog focuses on leveraging Snowflake as a data lake, its advantages, implementation methodologies, and how it outplays other competitors.

What is a Data Lake?

A data lake is a storage repository where we store structured, semi-structured, and unstructured data from which we can gain insights and analyze patterns in the future. In the digital era, data plays a vital role in any organization. Hence having a data lake becomes the need of the hour as we should not discard any data without reaping its benefits.

Challenges faced in the Data Lake

The main challenge we face with a typical data lake are:

  • Slow performance.
  • Data Integration.
  • No audit trails.
  • Difficulty in administration.
  • Data governance can contribute to poor management of these data lakes.
  • Lack of uniform security.
  • Access control across tools.
  • Inability to handle multiple data formats efficiently.

Snowflake as a Data Lake Solution

Snowflake, as a single platform, can be used to build data architecture by constructing logical data zones. Data from various data sources are pulled in using various tools like Snowpipe, Kafka connectors, or using batch copy. This data from the various source enter the first logical zone in Snowflake, which contains the Raw Data.

Snowflake as a Data Lake
Snowflake as Data Lake

This raw data is transformed into standardized data by applying typecasting and conversions, which could be used to build complex logical transformations in the later part. These zones typically constitute the “data lake” and the upcoming zones act as the Data Warehouse. The Data Warehouse consists of zones where data is transformed, cleansed, and modeled depending on the requirement and delivering the data as a set of tables or views. The raw layer becomes the data lake, where all the structured, semi-structured, and unstructured data are stored. No data is missed from the data source to make sure that it can be used to arrive at a business decision.

Snowflake provides various features and advantages which deliver the business results faster without compromising the costs as listed below:

  • Handling semi-structured data types (JSON, AVRO, XML, Parquet, and ORC.)
  • Kafka
  • Snowpipe for continuous loading of data from External Stages.
  • Streams that track the Change Data Capture of a Snowflake table.
  • Tasks are used to schedule various queries at a given time in a CRON job.
  • Materialized views for faster reporting.
  • External tables where we can query a table directly outside Snowflake.
  • Hive Metastore

In the next blog, we will look at how these features can be compared with other data lake solution providers like Amazon S3, Data Bricks/Delta Lake, and how Snowflake outplays them.

Learn more about Visual BI Solutions Snowflake offerings here and read more about similar Self Service BI topics here.


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