Conclusion

With the massive amount of data growth, organizations are focusing on driving greater efficiency in their operations by making data driven decisions. As data is coming from various sources in different forms, organizations are tasked with how to integrate terabytes to petabytes and sometimes exabytes of data that were previously siloed in order to get a complete view of their customers and business operations. Traditional on-premises data analytics solutions can’t handle this approach because they don’t scale well enough and are too expensive. As a result, customers are looking to modernize their data and analytics infrastructure by moving to the cloud.

To analyze these vast amounts of data, many companies are moving all their data from various silos into a Lake House architecture. A cloud-based Modern Data architecture on AWS allows customers to take advantages around scale, innovation, elasticity and agility to meet their data analytics and machine learning needs. As such, ingesting data into the cloud becomes an important step of the overall architecture. This whitepaper addressed the various patterns, scenarios, use cases and the right tools for the right job that an organization should consider while ingesting data into AWS.

Contributors

Contributors to this document include:

• Divyesh Sah, Sr. Enterprise Solutions Architect, Amazon Web Services

• Varun Mahajan, Solutions Architect, Amazon Web Services

• Mikhail Vaynshteyn, Solutions Architect, Amazon Web Services

• Sukhomoy Basak, Solutions Architect, Amazon Web Services

• Bhagvan Davanam, Solutions Architect, Amazon Web Services