Data Processing
Services

Real-Time Data Processing Capabilities

In today’s highly competitive business world, advantage is with organizations that can derive insights from all of their data very quickly. To be able to do this, you have to process hundreds of data streams bringing in millions of records at a rapid rate. Traditional systems are inefficient in handling this surge of hybrid data and serve the complex analytics needs of your organization. Whether you are just starting or scaling up, we provide the full set of services to get you up and running.

Evolving Data Ecosystem and Diverse Tool Chains

Major cloud platforms like AWS, Google, and Azure are driving significant innovation in the big data landscape. These platforms offer a rich ecosystem of tools and services to handle various aspects of data processing, from ingestion and storage to analysis and visualization. We help enterprises navigate this complex landscape and unlock the full value of their data.

AWS Kinesis, a scalable real-time data processing service, can handle a variety of data types, including video and IoT data. It provides features like Kinesis Data Streams for processing continuous data streams and Kinesis Firehose for loading data into data lakes and data warehouses. AWS Glue, a data integration service, reduces the overhead of managing servers or complex data pipelines.

Azure Databricks simplifies the process of working with Apache Spark on the Azure platform. It offers a collaborative workspace, pre-configured clusters, and optimized performance for data engineering, data science, and machine learning workloads.

Google Dataflow, a fully managed, serverless data processing service, provides a unified platform for both batch and streaming data processing. Built on the Apache Beam model, it allows you to define data processing pipelines in a scalable manner.

Adopted by more than 80% of all Fortune 100 companies, Apache Kafka is the leading streaming data platform today. Applications that require large-scale message processing benefit from this highly scalable and durable distributed messaging system. Low latency and data partitioning capabilities make Kafka useful in IoT, multi-player gaming, and website activity tracking, among a horde of other use cases.

Spark is the most widely used engine for handling real-time streaming data. By connecting Kafka event queue to Spark, a versatile data processing system can be created to support both batch and real-time data processing. Spark’s processing model is ideal for real-time interactive querying, graph computation analysis, and machine learning.

Databricks offers a workspace abstraction over Spark for data teams to work together seamlessly, share code, and visualize results in one place. This abstraction helps users leverage Spark's power more efficiently, reducing complexity and enhancing productivity in data projects. Optimizations like Photon engine and Delta Lake help enhance Spark's performance for faster data processing and querying.

While adept at both batch and stream processing, Flink’s more distinguishing qualities such as exactly-once guarantees and event time processing make it ideal for fault-tolerant and highly scalable streaming applications. It provides accurate results regardless of interruptions to data streams It achieves consistency in large-scale computation with negligible tradeoff between reliability and latency, spending minimal resources.