DATA LABB

The ETL (Extract, Transform, Load) tool is highly optimized to extract, transform, and load data from several sources into a data warehouse or a single data repository. The tool can extract data in its native format – unstructured, semi-structured, or structured – transform the data into a format that matches that of the intended target, and finally load it into the data repository. The SAP ETL tool can  also integrate different systems and transform various data formats into each other. The SAP ETL tool moves data from and to the SAP environment and runs checks to identify if the value of a name has been defined as well as clean the data. This works even when the data extracted is outside the application, a unique feature of the SAP ETL tool. The tool is used for migrating data from SAP or non-SAP sources to target the HANA database through the SAP Data Services. This enables businesses to run data analytics exclusively in the application layer. However, users should first defi...

Should you migrate databases from Microsoft SQL Server to Snowflake ? The answer is most definitely Yes, simply because of all the benefits that the cloud-based data warehousing solution Snowflake brings to the table. Here are some of the benefits that make organizations want to migrate databases from SQL Server to Snowflake. ·         Snowflake supports a wide range of cloud vendors as its architecture matches most of them. Users, therefore, do not have to use different tools to work on all or any one of the vendors.  ·         In the databases of the past, storage and computing facilities were all lodged in one silo. This made estimating costs of storage or computing very difficult. In Snowflake, these two are segregated and located in separate silos, thereby helping users accurately estimate the costs of maintaining either of them individually. ·         Migrating databases f...

In April 2020, SAP launched the HANA Data Lake that added more power to the existing cloud-based business ecosystem. Users benefitted from a cost-effective storage system from the package that consisted of a native storage extension and a relational SAP data lake. From the start, this data lake was considered to be as feature-rich as the existing players like Microsoft Azure and Amazon S3.  What makes SAP data lake stand out among its competitors is its unique architecture. Organizations have the option of storing data that is frequently used and regularly accessed in one layer while moving other not-so-critical data in low-cost storage layers of SAP HANA. Visualize the SAP data lake as a pyramid that has three layers. The top of the pyramid houses the most important data of an organization that is used in the regular course of business. Hence the cost of storing this data is the highest as it is frequently accessed for analytics or reporting. In the second layer of the ...

  In 2005, Microsoft introduced the “after date”, “after delete”, and “after insert” features in SQL Server that came to be known as SQL Server Change Data Capture. But it was not before 2008 that a new and improved version was launched that is still in vogue today.   How does SQL Server Change Data Capture   function? Primarily, it monitors and captures changes made to the tables in the SQL Server database without using any additional applications or programs. Microsoft, till 2016, offered this feature only in the high-end Enterprise edition but later became standard across all editions of SQL Server. Apart from the core function of SQL Server Change Data Capture   which is to track all Insert, Update, and Delete changes, the feature also records changes in a mirrored table with the same column structure as the source. One record is written by SQL Server for every Insert and Delete value while two records are written for every Update statement – data befor...

As a central repository that holds all the bid data from various different sources, a data lake is the vast, richest pool of raw data, the purpose of which is undefined. Data from this lake is then put to use for analytics and machine learning by organizations. A data lake is used for storing both relational and non-relational data from devices, apps, and users. Different types of analytics, such as machine learning, big data analytics, textual search, SQL queries, and real-time analytics can be derived from a data lake. It has both curated and non-curated data. An SAP data lake is easy to set up and can ingest data from on-premise or from a cloud warehouse. According to a survey, 90% of businesses believe that big data initiatives help them determine future success. A data lake allows data engineers, data scientists, and business analysts to access information using the tools and applications of their choice. There’s a lot of value in a data lake for businesses, the ability to harnes...