Your data, delivered your way. No matter how complicated your challenges are, our data integration expertise guarantees data accuracy in the exact format you need. Videal streamlines your organization's data and information flows, optimizing existing processes and integrating various data sources. Our expert consulting service enables your organization to adopt the best in class data integration technologies and solutions to drive tangible business outcomes.

Key features:

  • Define a roadmap to achieve the integration strategy;
  • Design of custom integrations between cloud and on-premise environments;
  • Project Management & Team experienced integration and data experts;
  • Data Quality Assessment and Improvement;
  • Data anonymization;
  • Quality metrics services;
  • Data migration;
  • Agile Delivery - Focused on user needs;
  • AWS professionals.

ETL flow:

  • Analyze, design, develop, and build custom ETL and data integration solutions that corresponds industry standards and best practices;
  • Extract data from multiple source formats, including Relational Databases, XML, Flat Files, Information Management Systems (IMS) through API, Virtual Storage Access Method (VSAM), and Indexed Sequential Access Method (ISAM);
  • Clean extracted data to ensure data integrity and eliminate so-called “raw data”;
  • Load transformed data into a customized data warehouse (EDW) in a manner that aligns with the client’s specific data storage needs;
  • Develop data warehouse structures and transformation logic from scratch;
  • Tune and modify existing workflows if needed;
  • Provide ongoing support and troubleshooting;

Technology stack:

  • Java, Scala, Kotlin and Python as programming languages;
  • Spring Boot, Data, MVC, Batch, Spring Cloud, Hibernate for creating scalable and high performance web applications;
  • Apache Spark, Apache PySpark for coding ETL algorithms;
  • AWS S3, EC2, EMR, Glue, Athena, Presto, Hive for data storage, clustering, running data processing and SQL queries; 
  • Apache Hadoop as a framework for the distributed processing of large data sets across clusters;
  • ElasticSearch, OpenSearch Elasticsearch for scalable text data store and RESTful search;
  • PostgreSQL, MySQL, MongoDB, Oracle DB for data storage and running SQL queries;
  • RabbitMQ, ActiveMQ, Redis for messaging implementation;
  • Kubernetes (K8s), Docker, CI/CD for DevOps;
  • Maven, Gradle, Groovy, Grails, Airflow as build automation tools;
  • Git for source code repositories versions control.