qlever
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qlever
Contact us
Integrations of data and applications,
data management
Solutions for transforming source data into a managed resource ready to receive analytical information

Why you need data integration

Even small companies have a lot of data created and saved using different methods. To access it, you need a solution that can load and prepare the data for further processing, as well as deliver it to users and applications. Data integration can remove the barriers between sources, target applications and business users. It does so by making data of different types accessible and ready for end-to-end use across the organization.

We use a wide variety of tools for integration. In order to lower project risks and speed up the ROI, we suggest the prepared modules and methodological approaches that have earned their place among data integration best practices.
  • Data lakes, Big Data infrastructure
    Traditional applications can’t work with massive and multiple data sets. However, data scientists and machine learning projects still need to use them.

    To solve this, we use toolsets and frameworks that can scale data processing onto a large number of computers, creating an ecosystem of interconnected projects and technologies.

    Large enterprises usually face the limits of legacy apps even when dealing with the simplest analytical tasks.

  • DataOps & Agile
    The DataOps methodology is the data equivalent of DevOps. Its goal is to organize a continuous process of accessing data and extracting information from it.

    DataOps helps you create a single process out of two main sets of tasks: the work done by your data collection and preparation specialists, and data analysis and subsequent application of conclusions to solving business tasks. It helps you organize the interactions between data architects, engineers, analysts, data scientists, and business users.

  • Data quality
    Low data quality in your sources is a common obstacle to the digital transformation of your business. Enterprises that juggle a large number of accounting systems have to start with source data quality in order to integrate the data and create analytical applications to process it.

    Usually, data quality control solutions are based on combining technology with the company’s organizational and analytical culture.

  • Data management and data catalogs
    Companies that deal with a large number of data sources have greater trouble functioning without solutions that focus on organizing work with data for different specialists and making that process transparent. This is where data catalogs come in. Modern catalog-creating platforms let you manage access to data sets, find the necessary data sets, use metadata, and control the quality of available data.

    Data management solutions show what lies behind your information, how to use that information, and how to supplement it.

Common data integration challenges

1. Uploading data from 500 POS terminals into an analytical application.
2. Combining ERP (e.g. Axapta or SAP) data with information from external sources.
3. Refactoring the BI solution to open new scalability possibilities.