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How to Optimize your Business Efficiencies with Snowflake Data Lake?

Snowflake Data Lake
Written by Sandip Lokhande

Cloud data platforms have several uses and functions that often balance and complement each other. Before going into Snowflake Data Lake, it is necessary for those who are new to this concept of cloud-based data platforms to understand what is data lake vis-a-vis data warehouse and what is the role of Snowflake here.

A data lake is a storage repository that is not only highly scalable but can also hold massive volumes of raw data in native format. The data in a data lake can be from a wide range of sources – unstructured, semi-structured, or structured format that can be queried whenever necessary. Many companies today need to store large volumes of data but do not need to process or generate analytics right away. Data lakes are very useful in these circumstances as it is a very effective solution to store data without any transformation.

Snowflake Data Lake

In the case of a data warehouse, on the other hand, data is processed and transformed for analytics and querying in a greater structured environment. This is why data warehouses are considered to be a perfect complement to data lakes. In the modern data-driven business environment, both data lakes and data warehouses play a major role and offer all the conveniences of the cloud such as reliability, security, and economies of scale.

Snowflake provides businesses with a secure and fast platform with all the functionalities of both data lake and data warehouse. You can position Snowflake Data Lake as the main data repository and get high performance and governance. Or as a Snowflake data warehouse, you can store data in AWS S3, Google Cloud Storage, or Microsoft Azure and speed up analytics and data transformation with Snowflake.

Benefits of Snowflake Data Lake

Snowflake Data Lake architecture is based on a cloud-based multi-cluster platform that meets all the specific needs of the prevailing business ecosystem. Being a single source of all data analytics requirements, it is a high-performance platform used by all the top organizations around the world. Here are some reasons why Snowflake data lake is so critical for businesses today.

  • Multiple workers can work simultaneously on different workloads, executing intricate queries and analytics without feeling any lag or drop in performance.
  • The SnowflakeDataLake architecture is so designed that it can hold large volumes of all data types – unstructured, semi-structured, and structured data like CSV, Parquet, JSON, and tables.
  • This data lake is highly elastic and users have access to computing resources without any limitations. The volumes used to change dynamically in real-time and the computing engine scales in and out automatically. Running queries during these times is not affected.
  • The Snowflake data lake is available as a complete managed service. Its data management features include data performance tuning, data protection, and data security. Businesses do not have to worry about data management systems at their level. Hence, Snowflake has complete data control that is expected from a cloud-based platform.
  • A big draw is a cost-effective storage option. Users pay only the base price as charged by the cloud service providers of Snowflake – Google Cloud Platform, Microsoft Azure, and AWS S3. Users have the option to scale up and down in storage volumes and pay only for the resources used.
  • On Snowflake, you can easily combine and move data around thereby ensuring data consistency and multi-statement transactions with cross-database joins.

It is therefore seen that there are multiple benefits of being on the Snowflake data lake platform. Some of the cutting-edge features include convenience, unlimited storage and computing, scaling and elasticity, high performance, and cost-effective pricing.

Running Queries on Snowflake Data Lake

You can run almost unlimited concurrent intricate queries on top of Snowflake data lake without affecting speed and performance. The other advantages that you have are –

  • Synchronizing external table by using Apache Hive meta store
  • Automatically registering new files with partition auto-refresh option directly from the data lake.
  • Directly querying data from the data lake without moving data through external tables.
  • Using materialized views over external tables to increase performance and execution speed in queries.
  • Boosting data exploration speeds with Snowsight. It is the in-built visualization UI of Snowflake.

Hence, Snowflake data lake is a storage repository that optimizes the operations of any organization.

Transform data effortlessly with Snowflake data lake

You can effortlessly build and run integrated and extendable data pipelines on Snowflake, process all data, and load it back into the data lake. Here are some of the possibilities in this regard.

  • Install pipelines for data processing and modern architecture to do so. It requires almost zero-maintenance.
  • Convert data quickly and efficiently with ANSI SQL
  • Automatically ingest data and set CDC (Change Data Capture) with constant data pipelines by using Streams and Tasks and Snowpipe.
  • Pipelines can be extended as per requirements with stored procedures and external functions.
  • Pipeline performance can be optimized by instantly scaling up or down
  • Build robust data pipelines with different data types and variant ingestion styles.

With the extendable data architecture in a single cloud environment, businesses do not have to choose between having a Snowflake data lake or a data warehouse.

About the author

Sandip Lokhande

I'm a Blogger and very much Innovative and up to date. Also I would like to write about Technology, Business, Software, Finance & Gadgets. You can connect with me on - techniciansnow1@gmail.com

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