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Projects

Data Quality

Data Quality is the biggest issue in Data Management. Even very focused projects on Customer Names and Addresses involve the use of several data sources in different formats with missing or conflicting values. Such projects require standardization, matching, extrapolation, and survivorship algorithms and methods to create the GoldenRecord. The enrichment is used to improve the matching/survivorship quality or to add business information. From a technological point of view, access to external data sources using REST API is an inherent part of the project.

Data Quality is the biggest issue in Data Management. Even very focused projects on Customer Names and Addresses involve the use of several data sources in different formats with missing or conflicting values. Such projects require standardization, matching, extrapolation, and survivorship algorithms and methods to create the GoldenRecord. The enrichment is used to improve the matching/survivorship quality or to add business information. From a technological point of view, access to external data sources using REST API is an inherent part of the project.

Data Migration

Data Migration is one of the most complex projects. It involves a very good knowledge of the source system and the target system. A Data Migration Project is not about migrating data from one platform to another. It's about reshaping the Data Model to reflect major business changes. One of the basic stages of a Data Migration Project is Data Quality. Another critical stage is Data Reconciliation that should be part of the of the initial planning and not part of the QA or UAT stage. Lately the Data Migration projects involve deep knowledge of actual technologies.

ETL Platform Migration

Many enterprises are migrating from Legacy ETL tools to Open Source Data Integration tools. The main reason for migration is that the Open Source tools are way ahead the Legacy DI tools. The Apache organization provides a well managed platform for incubating and promoting new technologies. Talend is based on many technologies developed and maintained by Apache.

Data Catalog

Talend Data Catalog gives your organization a single, secure point of control for your data. With robust tools for search and discovery, and connectors to extract metadata from virtually any data source, Data Catalog makes it easy to protect your data, govern your analytics, manage data pipelines, and accelerate your ETL processes. Data Catalog automatically crawls, profiles, organizes, links, and enriches all your metadata. Empower your data consumers to get right to the data. Data Catalog makes it easy to search and access data, then verify its validity before sharing it with peers.

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Solidatus gives you trust in your data and confidence in your decisions. Get fast insights with dynamic discovery, game-changing visualization and the ability to sustainably govern your complex data landscape.
By revealing hidden opportunities, threats and the impact of change, your Solidatus data blueprint will help you make the unknown known, so you can optimize your infrastructure, operate more efficiently and minimize risk.

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