lilymia79

A data lake is a data storage repository where data in its native form can be stored, regardless whether it is in an unstructured, semi-structured, or structured format. When required, this data can be easily accessed and processed for business analytics. If you use a highly optimized repository like the SAP data lake you get several benefits such as improved database performance, lower operating costs, and seamless access to data. Benefits of using the SAP data lake The main benefit of the SAP data lake is derived from its structure which is pyramid-shaped. Stored at the top of the pyramid is data that is frequently used and accessed daily for real-time processing and routine operations. The cost of storing this data is very high. At the middle of the pyramid is data that is not used daily but is important enough to be kept in the system and not deleted. The data storage costs are comparatively lower than the top segment. At the bottom of the pyramid, data that is very ra...

  Why do businesses move databases from SAP to Snowflake? There are a few reasons for this move. First, the nature of transactions in SAP often leads to execution delays, especially in cases where there are large database applications to handle. Next, access permissions are not clearly defined, leading to misperceptions amongst database administrators, and possible data breach scenarios. Given these downsides of SAP, it makes sense to move databases from SAP to Snowflake or other similar cloud-based platforms such as Google BigQuery or Azure Synapse. Let us now go through some benefits of moving databases to the cloud on Snowflake. ·         You get all the benefits of the cloud including access to cutting-edge technologies, unlimited data storage, data compression facilities, and automated fully-managed services. ·         SAP data can be stored in Snowflake in its native format, regardless of whether ...

Databricks is a software company founded by the creators of Apache Spark. Its USP is that it can combine data lakes and data warehouses into a Lakehouse architecture. The process involves manually building a data pipeline with a Python script typically through three easy steps.   ·       *   Setting up SQL Server as a source connector ·       *   Setting up Databricks Lakehouse as a destination connector ·       * Deciding which data to move from SQL Server to Databricks and the periodicity Before you can transfer data from SQL Server to Databricks, some basic functions have to be carried out. ·      *    Have a SQL Server account from where to transfer the data ·     *     Have a Databricks Lakehouse account as the target ·       *   An active cloud account Moving Data From SQL Server To Databricks Moving data ...