Without data integration, there is no digital transformation!
Is your company struggling with achieving its digital transformation goals? KASH Tech starts at the beginning to ensure that you have a solid data integration foundation. Kash Tech believes that data integration is platform-independent, and must have the capacity to integrate data regardless of the following:
→ Required frequency
→ Communication protocol
→ Business rules needed for intricate integration patterns
There are three primary types of products that support the data integration space:
Extract Transformation and Load (ETL):
These products are built to move large data volumes while offering the capability to carry out effective transformations. They are often found in a batch process algorithm. This is due to the availability of data volumes and the frequently used transformations like summation, aggregation, sorts, and multi-table joins.
Enterprise Application Integration Products (EAI):
These are often known as brokers or messaging software. These are designed in such a way that they move a smaller amount of data with amplified frequency patterns.
Enterprise Data Replication (EDR):
These products present information when data sets are modified or changed. These are delta-processing or change-data capture products. They often work on either a triggering or log-scraping mechanism. Also, they offer a pointer to the last extraction point for tracking the data that has been processed.
KASH Tech has the experience, and expertise in each of these fundamental data integration technologies to support your construction of a solid data foundation to support the digital transformation of your business.
Data modeling is the process of developing a visual representation of the data while showing the interdependencies between data points. This is the precursor to the development of the physical data model required to support the defined application solution.
As part of data modeling, the different types of data used, the relationship between the data points, and data grouping and organizing are displayed clearly. Business requirements are the guidelines around which data modeling is done.
In the initial stages of an application development project, data is clearly defined and organized in a logical structure. Following this, the data modeling steps are executed.
As part of data modeling, the type of application to be constructed for the specific business problem is identified. Some of the parameters to be considered while deciding on the type of model include:
→ Problem definition
→ Data collected
→ The type of output expected
→ Performance of the overall application
The standardized schemas and techniques used for data modeling ensure that it is a consistent and predictable method of managing data.
Data Modeling Advantages:
→ Provides clear definition and organization of data collected
→ Provides precise structure for the database thus reducing development errors in the future
→ Requires upfront consideration of the solution’s data requirements, expediting the database design and development process
→ Enhances overall solution performance
Our Data Modeling experts help businesses create models according to our defined hierarchy
Enterprise Data Architecture Design
A modern data architecture creates a foundation that enhances the access to – and extends the usage of – the large and disparate data stores from traditional and non-traditional sources. To overcome these common obstacles that are created from rigid and aging data silos, we design and build secure and adaptable architectures:
→ Inability to address business requirements quickly enough
→ Inability to process data in real-time, or near real-time
→ Difficulty handling big data (huge volumes, streaming, and multitude of data sources and types)
→ Discrepancies in how data is gathered, processed and used
→ Lack the infrastructure needed to support advanced analytics
Whether your environment is Cloud-based, on-premises, or hybrid – we design and build secure and flexible data architectures that promote the use of high-quality, relevant, and accessible data. Built to grow along with your business, a solid data architecture supports your analytics needs, including business intelligence, data science, custom applications, and regulatory reporting.
Data Virtualization represents the most modern approach to data integration. It establishes a new benchmark for fast, efficient, and effective access to disparate and distributed data sources within your organization. Unlike ETL solutions, which require data replication, data virtualization simply reveals an integrated view of all your business data to the appropriate users. There is no “fork lifting” of data from one source to another. On a real-time basis, and reacting to the requests of your decision makers, data virtualization retrieves the data from its original source. Data virtualization proves that connecting data is far superior to collecting it.
Logical Data Layer
Data virtualization provides a virtual approach to accessing, managing, and delivering data without replicating it in a physical repository.
Data virtualization integrates data siloed across all enterprise systems, regardless of data format, location, or latency.
Data virtualization delivers the integrated information in real time to the applications used by business users.
Data virtualization provides a centralized secure layer to catalog, search, discover, and govern the unified data and its relationships.
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