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In the prevailing business scenario, there are several benefits of backing up Microsoft SQL databases to the cloud like Amazon Web Services (AWS). It not only makes for easy restoration of databases using a cloud-based server but also offers almost unlimited storage space in the cloud. Apart from this aspect, why do organizations want to back up databases from Microsoft SQLServer to S3 , a platform that operates in the cloud? Benefits of SQL Server to S3 The first is affordable storage options. Amazon S3 provides access to various levels of data storage at proportionate rates. With S3 Storage Class Analysis, you can discover data that can be loaded to low or high-cost storage sections as per requirements. Next, after SQL Server to S3, you get excellent data scalability and durability. It is possible to scale up or down in storage resources by paying only for the quantum used. This takes care of fluctuating demands of storage without investing additionally in resource procurement cycl

The SAP Extractor is a program in SAP ERP that can be customized or taken from a standardized Data Source. Both options describe a delta load process or various types of full load. The SAP BW can remotely access the various data transfer facets of the SAP Extractor   and is used to capture and prepare data via an extract structure that is transferable to the Business Warehouse (BW) of SAP.  SAP data extraction with the SAP Extractor is of three types. The first is Content Extraction where the Extractor is used to extract BW content, FI, HR, CO, SAP CRM, and LO cockpit. Second is the  Customer-Generated Extraction where the SAP BW Extractor is used for LIS, FI-SL, and CO-PA. The third is Generic Extractionbased on DB View, Infoset, and Function Modules. The SAP Extractor was developed mainly to cater to the applications of the SAP Business Warehouse and the type of extraction activity used by organizations depends on their specific requirements. The SAP ECC system cannot be used for any

The Oracle database is used widely used as part of their IT stack and many organizations still prefer the on-premises database version. As data processing volumes increase, it is necessary to identify systems that can handle future growth. What is needed is to be on a platform that provides improved power and performance at a reasonable cost structure and the most optimized way to do so is to migrate databases in Oracle to SQLServer hosted in the Microsoft Azure cloud. Here are the steps to migrate databases from Oracle to SQL Server. Seamless migration of Oracle databases to SQL Server, Azure SQL Database, or Azure Synapse Analytics is done through the SSMA or the SQL Server Migration Assistant (SSMA) for Oracle. SSMA enables the review of database objects and data, access to databases for migration, and moving database objects to SQL Server, Azure SQL Database, or Azure Synapse Analytics. However, it is not possible to migrate SYS and SYSTEM Oracle schemas. Steps for Oracle to SQL S

  The SAP software consists of ERP software, application servers, technology stacks, and database systems. Hence, there is always a requirement to exchange data with other multiple sources. This process of retrieving data from an external source, modifying the format, and importing the data is known as Extract, Transform, and Load (ETL).      The most optimized SAP ETL tool helps SAP data to be available to organizations for analytics and business insights. The tool can also convert the complex data structure of SAP into an easily accessible data model because users can quickly transform a typically complex SAP data model with thousands of tables into a data replication activity at the transaction level. The SAP ETL tool is automated and provides high-performance solutions as it can move the right SAP data easily and securely at scale to any repository like a database, or data warehouse, regardless of whether they are on-premises or in the cloud.  ETL is Extract, Transform, and Lo

Change Data Capture ensures that not only is all data insulated from breaches or hacking but also that it is in a format that does not affect its history. To implement these aspects, different database management systemshave tried various solutions in the past like data audits, triggers, complex queries, and timestamps but none had offered a permanent answer. Microsoft was the first to make an effort in this direction when it launched its SQL Server Change Data Capture feature in 2005. The technology incorporated “after update”, “after insert”, and “after delete” capabilities which users found very complex. Subsequently, Microsoft introduced a revised version of SQL Server Change Data Capturei n 2008 that was well received and helped developers and DBAs capture and archive historical data and changes without requiring additional programming or other activities.  The Working of SQL Server Change Data Capture  The SQL Server Change Data Capture technology incorporates changes like upd

Data lakes are a form of structure that can store high volumes of ingested data to be processed and analyzed later. The concept of data today is no longer about different systems like data marts and legacy data warehouses. The introduction of Snowflake Data Lake has changed the complete data landscape by eliminating the need for developing, deploying, and maintaining different data storage systems. For the first time, a single enterprise-level cloud data platform can carry out seamless management of structured and semi-structured data like tables and JSON in an all-inclusive way. TheSnowflake data lake has an extendable data architecture that ensures fast data movement within a specific SAcloud-based environment. Data is generated via Kafka or another pipeline and persisted into a cloud bucket. From the bucket, a transformation mode and engine like Apache Spark transform the data into a columnar format like Parquet. It is then loaded into a conformed data zone. The advantage here is t