Cloud Data Engineering

While implementing Modern Data Platform, Coforge brings the solution on cloud data engineering where design, development and maintenance of the data processing happens on cloud platform. The goal is to create scalable and cost-effective data pipelines using Coforge’ s pre-defined frameworks to handle large volumes of data while providing real-time or near-real-time analytics and insights.

Coforge, by and large, brings the following solution components based on the architecture defined for the implementation:

• Data Ingestion : This is to collect data from heterogeneous data sources and move the data using various tools and technologies such as Apache Kafka, Amazon Kinesis, or Azure Data Factory.
• Data Storage : This is to store data in cloud-based data warehouses, data lakes, or databases using cloud platforms. The options are Amazon S3, Azure Blob Storage, or GCP Cloud Storage.
• Data Processing : This is to process and to transform the data using technologies like Apache Spark, Apache Flink, or Google Dataflow. This can handle large volumes of data in a scalable and efficient way.
• Data Analysis : Different analytics tools like Apache Hive, Apache Pig, or Google BigQuery are used to analyze and extract insights from data. This also helps in querying and visualizing large datasets in real-time.
• Data Governance : This is to ensure that data is secure, compliant, and accessible to authorized users. While bringing the solution on data governance, Coforge takes care on data quality management, metadata management, and data lineage to ensure that data is accurate and reliable.

Coforge takes care of following in end-to-end solution on cloud data engineering:

• Scalability
• Cost-effectiveness
• Real-time analytics
• Agility