What is Data Lake and what are the benefits that you will get by making Snowflake Data Lake an option for your database management? This post will elaborate on the many facets of Data Lake and Snowflake and the best practices to be followed in this regard.
Data architecture structures that can store massive volumes of data to be processed and analyzed at a later date are called data lakes. Previously data lakes had meant fragmented data marts, data warehouses, and more. But with the advent of modern technological innovations and developments, such differentiations are no longer required. This is especially useful in today’s data-driven business ecosystem that is fully dependent on optimized database management as huge volumes of data are routinely generated.
The primary advantage of data lakes is that it can store both structured and unstructured data. This enables businesses to get direct access to raw unfiltered data and process all in one place instead of having to go through various data silos. Data lakes have done away with the need to maintain multiple systems for various data types. This is more so on a cloud-based platform like Snowflake where it is very easy to manipulate data both structured and unstructured and holistically manage JSON and other tables.
Moreover, it is critical to move data in the usual data repositories through data zones but doing so poses a serious challenge to database administrators. This is where Snowflake Data Lakeis a big help for organizations.
Snowflake is a cloud-based data warehousing solution with multiple benefits.
First, Snowflake offers unlimited storage and computing facilities and users can scale up or down in the utilization of resources as per needs by paying only for the quantum used. Hence, for new projects, businesses do not have to invest in additional hardware and software but can effortlessly scale up on Snowflake. Next, because of this high-performing platform, several users can work and execute multiple queries simultaneously without facing any lag or drop in performance.
Additionally, Snowflake has an architecture that can be extended seamlessly to enable unhindered data movement within the same cloud region. For instance, any data generated via Kafka is transferred to a cloud bucket. From here, the data is converted to a columnar format with Apache Spark and this goes on to the conformed data zone thereby eliminating the work of businesses to choose from either a data lake or a data warehouse.
Why Snowflake Data Lake isconsidered ideal for organizations?
There are several advantages of a cloud-based data lake.
- Flexible approach – Computing resources in Snowflake are dynamic and flexible and vary depending on the number of users or the workload volume. The quantum of resources changes automatically as per need without affecting running queries. When there are times of heavy usage, the compute adjusts to the increased flows without any drop in performance.
- Single point data storage – Huge volumes of semi-structured and structured data like JSON, CSV, tables, Parquet, ORC, and more is easily ingested into Snowflake. There is no need for separate silos for data storage.
- Flexible data storage – Snowflake Data Lakehas highly flexible data storage capabilities. Only the base cost for using Snowflake cloud providers – Microsoft Azure, Amazon S3, and Google Cloud – has to be paid.
- Assured data consistency – Assured data consistency ensures that data can be easily manipulated and cross-database links with multi-statement transactions can be carried out.
As seen from these features of Snowflake Data Lake, users have the advantages of affordable computing and storage facilities and optimized scaling capabilities. However, matching the attributes of Data Lake and Snowflake in one is often a challenge. This is largely because the concept of Data Lake is almost a decade old spanning various business systems, countries, ecosystems, regions, and levels of data control whereas Snowflake is a recently-introduced, cloud-based platform.
Here are some of the benefits of Snowflake Data Lake
- Maximizing Data Lake strategy – Snowflake data warehouse can maximize any data lake strategy regardless of the location. A new feature of this cloud-provider is Database Replication. All databases can be replicated and maintained in sync within various regions and different cloud providers. In the case of an outage in one region of a primary database, a secondary database in another region is automatically triggered and work goes on without any downtime. When the outage is resolved, the same feature works in the reverse direction to update the primary database.
- Data portability – Users can move between regions or cloud providers easily and effectively. Data is also secured across regions in the cloud.
- Single operating system – Better data control is assured as the existence of a single cloud ecosystem ensures that the data lake can be expanded to take in global operations too if required. Hence, organizations can optimize their data management needs on a single Snowflake Data Lakeplatform spanning across regions and countries.
It is thus natural that organizations worldwide are switching to Snowflake Data Lake.