Nobody could deny the data driven aspect of business today and the impact the data has on the decision-making process. Thus, a cost-effective, scalable, and flexible platform for processing and analyzing large datasets is important.
Traditional on-premises data processing solutions can be expensive to set up and maintain. Amazon EMR offers a cost-effective, pay-as-you-go pricing model, allowing businesses to control costs by paying only for the resources they use for managing and configuring big data frameworks like Hadoop and Spark.
Notable features offered by AWS Cloud-based Architecture
High availability and flexibility
Scalability
Multiple services integration on one platform
Enhanced Security
Trades Fixed expenditure with variable
Ganit can help you maximise your decision velocity and minimise the decision risk through our extensive experience in providing cutting edge solutions to our clients.
We’ve successfully aided numerous clients in modernizing their data processing and analytics architecture by transitioning to a more efficient, scalable, and cost-effective AWS-based framework. Our primary focus remains on ensuring peak efficiency at every stage, from data ingestion to deriving valuable insights through analytics. AWS EMR (Elastic MapReduce) is often the preferred choice for many solutions. It offers a fully managed and highly scalable platform for processing large datasets. EMR seamlessly integrates with various analytical tools, minimizing manual intervention and delivering rapid results. We tailor the entire cloud architecture, including integrations, to meet each client's specific requirements.
At Ganit while we do strive to achieve operational and performance efficiency for our client, delivering tangible results to the clients through consistent effort remains our priority. Ganit made a substantial impact for a manufacturing company by orchestrating the seamless migration of 7,000 tables totaling 5 terabytes of data from their on prem servers to Amazon Data lake via DMS. Additionally, we designed the Euclidean framework, powered by PySpark, to perform ETL tasks efficiently using S3, Step Functions, EMR, and Redshift, with scheduled job execution through EventBridge resulting in a remarkable 20% reduction in manual errors.
See the impact that we make on our
cross-industry client base.
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