Articles

SAP S/4HANA, Microsoft SQL Integration and Hard Deletion Handling

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This article will demonstrate the heterogeneous systems integration and building of the BI system and mainly talk about the DELTA load issues and how to overcome them. How can we compare the source table and target table when we cannot find a proper way to identify the changes in the source table using the SSIS ETL Tool?

Systems Used

  • SAP S/4HANA is an Enterprise Resource Planning (ERP) software package meant to cover all day-to-day processes of an enterprise, e.g., order-to-cash, procure-to-pay, finance & controlling request-to-service, and core capabilities. SAP HANA is a column-oriented, in-memory relational database that combines OLAP and OLTP operations into a single system.
  • SAP Landscape Transformation (SLT) Replication is a trigger-based data replication method in the HANA system. It is a perfect solution for replicating real-time data or schedule-based replication from SAP and non-SAP sources.
  • Azure SQL Database is a fully managed platform as a service (PaaS) database engine that handles most of the management functions offered by the database, including backups, patching, upgrading, and monitoring, with minimal user involvement.
  • SQL Server Integration Services (SSIS) is a platform for building enterprise-level data integration and transformation solutions. SSIS is used to integrate and establish the pipeline for ETL and solve complex business problems by copying or downloading files, loading data warehouses, cleansing, and mining data.
  • Power BI is an interactive data visualization software developed by Microsoft with a primary focus on business intelligence.

Business Requirement

Let us first talk about the business requirements. We have more than 20 different Point-of-Sale (POS) data from other online retailers like Target, Walmart, Amazon, Macy’s, Kohl’s, JC Penney, etc. Apart from this, the primary business transactions will happen in SAP S/4HANA, and business users will require the BI reports for analysis purposes.

Source de l’article sur DZONE

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Understanding MongoDB and NoSQL Database

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NoSQL data sets arose in the latter part of the 2000s as the expense of storage drastically diminished. The days of expecting to create a complicated, hard to-oversee data model to avoid data replication were long gone and the primary expense of programming and development was now focused on the developers themselves, and hence NoSQL databases were brought into the picture to enhance their productivity.

As storage costs quickly diminished, the measure of data that applications expected to store increased, and the query expanded as well. This data was received in all shapes and sizes — organized, semi-organized, and polymorphic — and characterizing the schema ahead of time turned out to be almost incomprehensible. NoSQL databases permitted the developers to store colossal measures of unstructured data, providing them with a ton of flexibility. 

Source de l’article sur DZONE

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MySQL to Redshift: 4 Ways to Replicate Your Data

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MySQL is the most popular open source cloud database in the world, and for good reason. It’s powerful, flexible, and extremely reliable. Tens of thousands of companies use MySQL to power their web-based applications and services every day.

But when it comes to data analytics, it’s a different story. MySQL is quickly bogged down by even the smallest analytical queries, putting your entire application at risk of crashing. As one FlyData customer said to us, “I have nightmares about our MySQL production database going down.”

Source de l’article sur DZONE

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