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Data Integration, ETL and Data Virtualization

The more fragmented data exists in the company, the more important it becomes to integrate technical and business data into a uniform, easy-to-query schema.Get in touch now

Merge data optimally!

Distributed across numerous systems, vast amounts of data records with potentially business-critical information are created every day. The challenge here is to describe and merge data. The more fragmented the data in a company, the more important technical and functional data integration becomes. This requires a uniform and easily retrievable schema.

Therefore, consult our data engineers - because proper data engineering as well as data analytics will bring you to the top of your business sector.

What is Data Integration?

Data Integration describes all measures, tools or processes that are necessary to transfer data from source systems into a target system (often data warehouse or data lake).

This usually includes options for connecting to the source system ("connectivity"), different speeds (batch vs. real-time) and logic for transforming the data or bringing it into a uniform schema.

What do the abbreviations ETL and ELT mean?

The abbreviation "ETL" stands for "Extract, Transform, Load": In this integration approach, data is extracted from various sources, transported into and out of a middleware and then converted into the desired format. The data is then integrated into a database or data warehouse. In "ELT" ("Extract, Load, Transform"), the middleware plays a different role: it generates the code optimized for the database technology used, which performs the transformation directly. The data remains in the database, but the middleware retains control over the process.

Today, instead of ETL or ELT, the term "data integration" is often used to name all data integration methods in one expression:

ob batch,
in real time
inside or outside a database
or between any systems.

What is Data Management?

Data management is the secure capture and storage of data. Data management includes measures to ensure data quality and data lifecycle management.

Data Integration vs. Data Virtualization

ETL

Extract, transform and load - extracted data is transformed by a middleware ETL server before being transferred to the target system.

Data Replication

Changes to the source system are replicated to the target system in real time.

Publish-Subscribe

Downstream systems subscribe to a data integration service that updates the target system at regular intervals.

API and web services

API and web services are used to build a loosely coupled architecture that accommodates multiple request- and response-based data services simultaneously

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Standardized data management creates basis for reporting

TIMETOACT implements a higher-level data model in a data warehouse for TRUMPF Photonic Components and provides the necessary data integration connection with Talend.

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Our Success Stories

Key points of Data Integration

Data Virtualization - modern and flexible data management

In addition to the physical integration of data, the purely logical integration "Data Virtualization" can also be found due to the higher flexibility and agility, especially in modern "Data Factory" architectures.

Data Virtualization integrates your data from distributed sources as well as locations. Even if different formats are available. Your data does not have to be replicated. With Data Virtualization, flexible, comprehensive data preparation and analysis is possible, from historical data of a data warehouse to operational data.

Data Integration vs. Data Virtualization

In classic Data Integration, data is physically transferred from the source to the target. The advantage of this is that it provides shared access with assured performance.

For Data Virtualization, the data remains in its original location. A logical data model thus replaces the physical one. The agility gained is bought by performance challenges and limited transformation logic.

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5 steps to integrate your data:

The success of an analytical project is based on an appropriate architecture. After the design of a data model, data integration is the most costly building block in the realization. All analytics is based on it, research shows that 70-80% of the effort goes into the design and implementation of data integration.

Virtualization of the steps for data integration

Our Data Integration Services

Of course, we support each customer individually in his or her requirements: holistically in the context of a data warehouse / data lake project, in the development of an analytical infrastructure including tool selection or concrete implementation of data integration processes. Especially in the context of a larger project, it has proven to be useful to understand the exact requirements in workshops in order to contribute our experience in a targeted way to a solution, to propose an architecture, to implement it and to further develop it together with our customers.

Expertise

Some of our certified experts have more than twenty years of experience in developing data integration processes.

Coaching

Our goal is to enable our customers to understand and, if necessary, develop their own processes. It is their business that can be improved through analytics.

Implementation

Agile approach has proven itself in implementation. The mixture of pragmatism and close involvement of the customers leads to a fast project success.

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The right vendor for every project:

Are you looking for technical support on Data Integration, ETL and Data Virtualization? We work with technologies and commercial solutions from the following vendors:

IBM

IBM "Information Server", InfoSphere" and "DataStage" are technologies that have been used for data integration projects for decades. The solutions are comprehensive, mature and highly integrated with each other. Functions for data integration, data governance, data quality and also a data catalog complete the offering, and they are not limited exclusively to data warehouses, which have also been implemented in IBM technology (Db2, NPS and others). In the recent past, IBM has focused mainly on the integration of tools in the AI platform "Cloud Pak for Data", i.e. all technology modernized, containerized and even more tightly integrated. In particular, the functions have been extended by modern approaches, e.g. AI for quality detection or data virtualization.

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Microsoft

Microsoft clearly divides the data integration functions into "on-premise" or as a Cloud service within Azure. Within the MS SQL Server, the "Integration Services (SSIS)" have been used for many years, which are completely integrated into the SQL Server and the well-known development interface from Microsoft. With the creation of numerous functions for data integration in Azure, Microsoft has largely fulfilled the demand for a complete solution in the Cloud: the "Azure Data Factory" has comprehensive possibilities for data integration, sensibly in an Azure-centric architecture and embedded in the integrated analytics workbench "Azure Synapse Analytics".

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Talend

Talend has developed into a comprehensive provider of a data integration platform for Big Data in recent years. While Talend was known to most customers a few years ago as an open source variant for simple to moderately complex data integration tasks, today all aspects of modern data integration are served by Talend. In particular, Talend is known for its extensive connectivity to data sources and targets, making it an excellent platform for enterprise application integration (EAI). Tight integration with proprietary data governance tools, such as the Data Catalog, complements the platform.

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Contact us now!

We would be happy to advise you in a non-binding meeting and show you the potential and possibilities of Data Integration. Simply leave your contact details and we will get back to you as soon as possible.

Martin Clement

[email protected]

Data Integration, ETL and Data Virtualization

Merge data optimally!

What is Data Integration?

Data Integration vs. Data Virtualization

ETL

Data Replication

Publish-Subscribe

API and web services

Standardized data management creates basis for reporting

Our Success Stories

Key points of Data Integration

5 steps to integrate your data:

Our Data Integration Services

Expertise

Coaching

Implementation

The right vendor for every project:

IBM

Microsoft

Talend

Contact us now!

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