The enormous volumes of data created and maintained by industries or research institutions outgrew its infrastructure capabilities and is increasingly dependent on the extensive use of big data architectures.

Ingest, prepare, and process data pipelines at scale for Artificial Inteligence and Analytics in the cloud.

For Agile methodology which focuses on collaboration, customer feedback, and rapid releases, DevOps and DataOps plays a vital role in bringing development process and operations teams together into data analytics so that data teams and users work together more efficiently and effectively addressing the unique needs of data and analytics environments.

Whereas Agile and DevOps relate to analytics development and deployment, DataOps manages and orchestrates a data pipelines as a manufacturing line where quality, efficiency, constraints and uptime must be managed.

Scalable and efficient data processing pipelines are important for the success of analytics, data science and machine learning.

Architecture and Solutions for High-Throughput, Low-Latency, Big Data Pipelines on premisses and in the Cloud

DevOps is an approach to software development that accelerates the build lifecycle (formerly known as release engineering) using automations to improve the quality and cycle time of code releases. Optimizing code, builds and delivery is only one piece of the larger puzzle for data analytics. DataOps seeks to reduce the end-to-end cycle time of data analytics, from the origin of ideas to the literal creation of charts, graphs and models that create value. The data lifecycle relies upon people in addition to tools. For DataOps to be effective, it must manage collaboration and innovation. To this end, DataOps introduces Agile Development into data analytics so that data teams and users work together more efficiently and effectively.

Comprehensive data engineering portfolio provides tools to process and prepare big data engineering workloads to fuel analytics and Artificial Inteligence: robust data integration, data quality, streaming, masking, and data preparation capabilities.

Data engineers can help data scientists and data analysts by:

  • Finding the right data and making it available in their environment.
  • Ensuring the data is trusted and sensitive data is masked.
  • Operationalizing data pipelines and helping everyone spend less time preparing data.
  • Improve quality standards and data integrity, ensuring that each technology component behaves in a consistent and reproducible manner.