The Reasons For Having Your Data Lake on Snowflake

 

The concept of Data Lake was introduced more than a decade back and due to its many cutting-edge features immediately had a positive impact on the business community. It was unheard of in those times to have all unstructured, semi-structured, and structured data in one single repository. But it took some time before data lakes could get over the initial teething problems and become a viable alternative to the existing data marts and data warehouses.



When Snowflake Data Lake was presented to the business community, they found that it was a solution to all their data warehousing issues and was exactly what they were looking for. The architecture of data lakes can store high volumes of data. These can be processed and analyzed at any time to get detailed insights about the various aspects of an enterprise. All the data are stored in one single location as against the earlier versions where they lay scattered in various silos such as data marts and data warehouses.

The Snowflake Data Lake architecture can be extended to allow effective data movement in the same cloud ecosystem. An example will clear the point better. For example, consider that data generated via Kafka is placed in a cloud bucket. Now, from this bucket, the data can be transformed into a columnar format and persisted into the conformed data zone. This helps businesses avoid the worries of having to choose between a data lake and a data warehouse. Snowflake can adjust automatically to unlimited data storage and computing requirements.

Given all benefits accrued to the world’s leading businesses, it is hardly surprising that they have opted for Snowflake Data Lake as their data management system.

Comments

Post a Comment

Popular posts from this blog

What is Oracle Change Data Capture and its Benefits

Image
  In the past, businesses have introduced advanced technologies in their database management systems. However, it was soon realized that getting insights into real-time data was not easy. Replacing existing databases was more than they could handle even though the data and transactions were critical for data analytics. Over the last couple of decades, it was seen that both the velocity of data and the volumes were increasing dramatically and exponentially. It became essential to opt for cloud-based solutions and Change Data Capture (CDC) from databases like Oracle, MySQL, and SQL Server. It was seen that Oracle CDC was one specific point where the effect of modern data integration use cases was the largest. Soon, an increasing number of organizations chose to move to event-driven architectures to maximize the dynamic distributed capabilities of databases, particularly Oracle CDC. One of the main benefits of Oracle CDC is that events that are created can be extracted in real-time.
Read more

Features of Tools for Oracle Database Replication to Snowflake

Image
  Replicating the Oracle database to Snowflake is a preferred option for most businesses because of the many benefits that the cloud-based data warehousing solution Snowflake has to offer. It is a high performing platform where multiple users can work simultaneously on several intricate queries without any drop in performance. Further, Snowflake provides separate computing and storage facilities so that users can scale up or down in either of them by paying only for the quantum of resources used. However, for replicating the database Oracle to Snowflake , it is essential to use the most optimized tools to not only automate the process but also to ensure that it is done quickly, effectively, and seamlessly. Here are some of the features that the best tools for replication will have. ·         Should be able to handle large volumes of data thereby speeding up the Oracle to Snowflake process . Once replication is done without the intervention of Database Administrators, the cost of
Read more

Change Data Capture Feature in Oracle Database – Evolution and Benefits

Image
  The Change Data Capture feature in the Oracle database platform is an integrated technology introduced in the 9i version. This new feature tracked and recorded all modifications in user tables in a database. These changes were then stored in ETL applications in the changed tables. The change data was processed and loaded into target data warehouses and other databases. The first model of the Oracle CDC technology worked on triggers set in the source tables that captured any changes as soon as they were made. However, this method was found to be too invasive by Database Administrators who were not very willing to work with it. Shortly thereafter, Oracle released a new form of Oracle CDC with the 10g version. It was different from the earlier model as the new one worked by leveraging the redo logs of the source database. This built-in tool for Oracle CDC was called Oracle Streams. This technology was a great improvement over the earlier one as it could detect and transfer change d
Read more