Our architects analyse and qualify the Use Cases, identify the data sources, define the data ingestion strategy & acquisition, plan & design data storage and data processing pipelines, establish an information security strategy and choose different forms of data consumption outputs.
Great Reading: Data TeamsIncrease the ability to deliver applications and services at high velocity by merging development and operations in one team. Our devops engineers work across the entire application lifecycle, from development and test to deployment to operations and have a range of skills not limited to a single function.
Build secure data lakehouses fastOur Data Engineers choose the right technologies to build scalable Data Pipelines and generate Data Products. DataOps works with the Analytics team to consume the Data Product in order to derive insights that drives business decisions.
10 Proven Steps to become a Data Engineer
Data streams can be processed on a record-by-record basis or over sliding time windows, and used for a wide variety of analytics. Information derived from such analysis gives companies visibility into many aspects of their business, (near) real time, making the organizational decision making processes multi-fold faster. Our engineers have deep expertise with designing, building and operating stream processing solutions.
Learn moreOur developers help you with the vision, definition, design, roadmap and development of your new End to End Data Driven Applications. We deeply understand and work with a variety of industry leading tools across the software development lifecycle spectrum.
Example Application
Our IT integration consulting service harnesses generative AI to seamlessly integrate advanced algorithms into your existing systems. By incorporating AI-generated content and automation, we optimize processes, increase data insights, and provide tailored solutions to elevate your business's technological capabilities.
The Power of AI
Data and analytics have become a competitive differentiator and a primary source of value generation for organizations. However, transforming data into a valuable corporate asset is a complex topic that can easily entail the use of dozens of technologies, tools, and environments. AWS provides the broadest and deepest set of managed services for data lakes and analytics, along with the largest partner community to help you build virtually any data and analytics application in the Cloud.
With origins in academia and the open source community, Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.
Founded by the original developers of Apache Kafka, Confluent delivers the most complete distribution of Kafka with Confluent Platform. Confluent Platform improves Kafka with additional community and commercial features designed to enhance the streaming experience of both operators and developers in production, at massive scale.
Azure analytics services enable you to use the full breadth of your data assets to help build transformative and secure analytical solutions at enterprise scale. Fully managed services like Azure Data Lake Storage Gen2, Data Factory and Databricks, help you easily deploy solutions for BI and reporting, advanced analytics, and real-time analytics. dkfjmsqdjfk
At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower people to transform complex data into clear and actionable insights. Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.
The MinIO Enterprise Object Store is built for production environments where everything matters - performance, security,scale and manageability. Cloud-native by design, it is ideal for large scale AI/ML infrastructure, modern data lakes and data lakehouses anddatabase workloads. It is software-defined and runs on any cloud - private, public, colo or edge.