Ahana Cofounders Make Data Predictions for 2021

Ahana’s Cofounder and Chief Product Officer, Dipti Borkar, and Cofounder and Chief Technology Officer, Dave Simmen predict major developments in cloud, data analytics, databases and data warehousing in 2021.

As the shift to the cloud and multi-cloud environments has become even greater during the past year hastened by the challenges of the COVID-19 pandemic, new challenges have arisen when it comes to managing data and workloads. Companies want to keep their data secure in their own accounts and environments but still leverage the cloud for analytics. They want quick analytics on their data lakes. They also want to take advantage of containers for their applications across different cloud environments.

Dipti Borkar, Co-founder and Chief Product Officer, outlines the major trends she sees on the horizon in 2021:

• Open Source for Analytics & AI – Next year will see a rise in usage of analytic engines like Presto and Apache Spark for AI applications because of its open nature – open source license, open format, open interfaces, and open cloud.
• Open Source for Open Analytics – More technology companies will adopt an open source approach for analytics compared to the proprietary formats and technology lock-in that came with the traditional data warehousing. This open analytics stack uses open source Presto as the core engine; open formats such as JSON, Apache ORC, Apache Parquet and others; open interfaces such as standard JDBC / ODBC drivers to connect to any reporting / dashboarding / notebook tool and ANSI SQL compatibility; and is open cloud.
• Containerizing Workloads for Multi-Cloud Environments – As the importance of a multi-cloud approach has gained traction over the past year, next year more companies will run container workloads in their multi-cloud environments. To do that, they’ll need their compute processing to be multi-cloud ready and containerized out of the box, so choosing a compute framework will become even more critical for these workloads. Engines like Presto, which are multi-cloud ready and container friendly, will become the core engine of these multi-cloud containerized workloads.
• Less Complexity, More Kubernetes-ity for SaaS – Containers provide scalability, portability, extensibility and availability advantages, but managing them is not seamless and, in fact, is often a headache. Kubernetes takes that pain away for building, delivering and scaling containerized apps. 2021 will bring more managed SaaS apps running on K8s, and those that are able to abstract the complexities of their platforms from users will emerge as the winners.
• The New “In-VPC” Deployment Model – As cloud adoption has become mainstream, companies are creating and storing the majority of their data in the cloud, especially in cost-efficient Amazon S3-based data lakes. To address data security concerns, these companies want to remain in their own Virtual Private Cloud (VPC). As a result, 2021 will bring in a new cloud-native architecture model for data-focused managed services, which I’m calling the “In-VPC” deployment model. It separates the control plane from the compute and data planes for better security and cleaner management.

Dave Simmen, Co-founder and Chief Technology Officer, outlines the major trends he sees on the horizon in 2021:

• The Next Evolution of Analytics Brings a Federated, Disaggregated Stack – A federated, disaggregated stack that addresses the new realities of data is displacing the traditional data warehouse with its tightly coupled database. The next evolution of analytics foresees that a single database can no longer be the solution to support a wide range of analytics as data will be stored in both data lakes and a range of other databases. SQL analytics will be needed for querying both the data lake and other databases. We’ll see this new disaggregated stack become the dominant standard for analytics with SQL-based technologies like the Presto SQL query engine at the core, surrounded by notebooks like Jupyter and Zeppelin and BI tools like Tableau, PowerBI, and Looker.
• SQL Is the New…SQL – As companies shift their data infrastructure to a federated (one engine queries different sources), disaggregated (compute is separate from storage is separate from the data lake) stack, we’ll see traditional data warehousing and tightly coupled database architectures relegated to legacy workloads. But one thing will remain the same when it comes to this shift – SQL will continue to be the lingua franca for analytics. Data analysts, data engineers, data scientists and product managers along with their database admins will use SQL for analytics.