Data Architecture:

According to the Data Management Body of Knowledge (DMBOK), Data Architecture “includes specifications used to describe existing state, define data requirements, guide data integration, and control data assets as put forth in a data strategy.” Data Architecture bridges business strategy and technical execution, and according to our 2017 Trends in Data Architecture Report: “Data Architecture is as much a business decision as it is a technical one, as new business models and entirely new ways of working are driven by data and information.”

Data Architecture can be synthesized into the following components:
  • Data Architecture Outcomes: Models, definitions, and data flows on various levels, usually referred as Data Architecture artifacts.
  • Data Architecture Activities: Forms, deploys, and fulfills Data Architecture intentions.
  • Data Architecture Behaviors: Collaborations, mindsets, and skills among the various roles that affect the enterprise’s Data Architecture.
Data Modeling:

Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data needs to flow. The diagram can be used to ensure efficient use of data, as a blueprint for the construction of new software or for re-engineering a legacy application.

Data modeling is an important skill for data scientists or others involved with data analysis. Traditionally, data models have been built during the analysis and design phases of a project to ensure that the requirements for a new application are fully understood. Data models can also be invoked later in the data lifecycle to rationalize data designs that were originally created by programmers on an ad hoc basis.

ETL Development

An ETL Developer is an IT specialist who designs data storage systems for companies, and works to fill that system with the data that needs to be stored. ETL stands for “extract, transform, load,” which is the process of loading business data into a data warehousing environment, testing it for performance, and troubleshooting it before it goes live. ETL Developers must be experts at taking a big-picture view of a company’s data situation to come up with comprehensive data storage solutions.

ETL Developers generally work as part of a team. They are sometimes employed by a single company. Or, they may act as consultants to multiple organizations. According to the Bureau of Labor Statistics and demand for Database Administrators, which includes ETL Developers, is expected to increase 11 percent through 2024. This growth will be driven by the increased data needs of companies, which will also increase the need for ETL Developers.

Performance Tuning:

Performance tuning is the improvement of system performance. Typically in computer systems, the motivation for such activity is called a performance problem, which can be either real or anticipated. Most systems will respond to increased load with some degree of decreasing performance. A system's ability to accept higher load is called scalability, and modifying a system to handle a higher load is synonymous to performance tuning.

Systematic tuning follows these steps:
  • Assess the problem and establish numeric values that categorize acceptable behavior.
  • Measure the performance of the system before modification.
  • Identify the part of the system that is critical for improving the performance. This is called the bottleneck.
  • Modify that part of the system to remove the bottleneck.
  • Measure the performance of the system after modification.
  • If the modification makes the performance better, adopt it. If the modification makes the performance worse, put it back the way it was. This is an instance of the measure-evaluate-improve-learn cycle from quality assurance.
  • A performance problem may be identified by slow or unresponsive systems. This usually occurs because high system loading, causing some part of the system to reach a limit in its ability to respond. This limit within the system is referred to as a bottleneck.
  • A handful of techniques are used to improve performance. Among them are code optimization, load balancing, caching strategy, distributed computing and self-tuning.
Database Administration:

Database administration is the function of managing and maintaining database management systems (DBMS) software. Mainstream DBMS software such as Oracle, IBM DB2 and Microsoft SQL Server need ongoing management. As such, corporations that use DBMS software often hire specialized information technology personnel called database administrators or DBAs.

  • Installation, configuration and upgrading of Database server software and related products. Evaluate Database features and Database related products.
  • Establish and maintain sound backup and recovery policies and procedures. Take care of the Database design and implementation.
  • Implement and maintain database security (create and maintain users and roles, assign privileges). Database tuning and performance monitoring. Application tuning and performance monitoring. Setup and maintain documentation and standards.
  • Plan growth and changes (capacity planning). Work as part of a team and provide 24x7 support when required.
  • Do general technical troubleshooting and give cons.
INCORTA: Introducing the united data analytics platform

Incorta is a radical, modern approach to the concepts of analytics and business intelligence. As an end-to-end analytics platform, Incorta sources data, stores data, and provides a visualization interface for analytics all in a single platform.

Where traditional approaches require complex modeling and reshaping of data, Incorta allows you to analyze the data in its original shape and preserves the original data fidelity. If your underlying data source has 190 tables with 358 joins across some four-billion rows of data, Incorta allows for direct analysis of that data with all the joins occurring dynamically and access to all of the detailed transactions. Further, the analyses, including dynamic aggregations on that data, occur in seconds or sub-seconds. There are no star schemas or cubes.

By leveraging data in shape, Incorta lets you deploy an analytic solution quickly and using a much simpler process compared to traditional technologies. With traditional approaches, the data is extracted from the source system (usually using an ETL tool), modeled into a data warehouse or data mart structure, a semantic model is created, and finally, reports and dashboards are put in place. It is not uncommon for this process, in a traditional deployment to take 37-88 weeks (or more).

Incorta, conversely, removes many of those steps. Data is loaded, in shape, to Incorta. An optional semantic layer can be placed on top of the data to group like elements for analysis, and then users leverage both packaged and self-service/ad hoc reporting capabilities. With Incorta, deployments average 6-18 weeks in total.

Business Benefits
While Incorta’s technology is revolutionary, the real business value comes from simplified deployment processes and effective, timely use of data. Incorta:
  • Removes complex ETL by keeping the data in shape. This allows for dramatically reduced deployment times.
  • Represents a modern alternative to the data warehouse. All your transactional data is available for analysis and collaboration across your enterprise.
  • Allows users to leverage integrated search to filter and perform data discovery.
  • Supports near real-time access to data by allowing for incremental data loads as frequently as every 5 minutes.
  • Fulfills the decades-old promise of true user self-service, ad hoc analysis. Most Incorta deployments require limited maintenance activities by IT.
  • Enables users to quickly cleanse and enrich data from source systems without requiring complex ETL logic or straining existing database resources.