Over the past few years and especially in the year gone by, businesses have been exposed to a multitude of disruptions, uncertainties, and disorders that have forced them to relook at the core and edges of their IT operating model and make rapid strides towards the adoption of a multi-cloud or hybrid cloud architecture. Many research studies have revealed that a data-first multi-cloud-centric strategy can deliver scalable costs and performance benefits.
What this transformation entails is that enterprise application and business data will be hosted on varied platforms and pose a new set of questions to the IT operations and operations and database administrators, including:
How to align the unique requirements of the federated data with the best multi-cloud deployment model that can consolidate all enterprise data into a lake without increasing costs and enable users to generate insights and visualization.
How to manage the spiraling data analytics infrastructure costs that come with managing disparate database management systems, whether on-prem or in cloud?
How to optimize ETL, ELT, and querying processes to utilize the full potential of business use cases?
AWS Athena is a service that delivers easy-to-analyze data analytics capabilities on data lake on S3 by offering ad hoc querying to deliver instant results. Being a serverless data query service makes it scalable and cost-effective, and thereby much easier to use without needing to worry about infrastructure deployment and management. Options to use a federated query across a variety of data sources external to Amazon S3 such as on-prem Teradata, Amazon Redshift, Google BigQuery, Oracle, Snowflake, Cloudera, or SAP HANA amplifies the data analytics capabilities and decision-making abilities.
Traditionally, querying a table in a different database would require significant infra and resource bandwidth. All relevant data would be transferred via an extract-transform-load (ETL) pipeline, pulling data from the source database, transforming it in-transit, and saving it to the target database ready for querying. If the three databases in our example were cross-referencing each other repetitively, this could significantly increase compute, storage, and network resource costs on AWS.
In our endeavor to help business leaders and organization reimagine value to stakeholders and transformation with digital technologies, we are proud to introduce Trianz AFQ Connectors, a comprehensive library of hybrid connectors to access data in S3 and across on-prem and other public cloud environments and deliver exceptional query performance and ad-hoc data exploration. These connectors are built for enterprises to transform their analytics on cloud capabilities by helping them realize the benefits of hybrid cloud and data federation and empower the business users to create meaningful insights from stored data.
Federated queries work differently. The query is parsed and targeted to a specific data source using a federation layer. Imagine the federation layer as a translator: if someone searches for a data entry on a customer, the federation layer will send a sub-query to every connected database. The sub-query is derived from the user’s federated query and is automatically converted to the target language for each target database.
The benefits are immense compared to traditional querying approaches.
First, users do not need to remember credentials or log into individual databases. Everything is centralized within the federated query service, providing unified access to data across all source types and IT environments.
Second, data scientists and analysts can analyze data in place with federated queries. Athena federated queries are optimized before execution using sub-queries and DDL conversion, enabling hundreds of user queries to be load-balanced and de-duplicated in real time. This enables higher throughput and lowers costs when using advanced analytics or business intelligence (BI) tools—promoting insight generation and data-driven decision-making.
Third, users do not need to know the specific query language for each database. Automated DDL conversion allows non-technical audiences to perform queries on all data sources. This extends analytics and BI capabilities beyond the data science department, enabling any user in any department to execute queries and perform analytics. When adding third-party solutions like our partner Tableau, users can even use a no-code SQL syntax to generate queries without needing to learn a data language.
The Trianz AFQ Connectors are built to expedite time-to-market, minimize development effort, and enable greater customer-centricity. Our solution expands upon existing Amazon Athena support, enabling data connections to Teradata, Snowflake, Google BigQuery, SAP HANA, and Oracle DB. All these connectors have been tested and tried for large and complex data sets in Fortune 1000 and the results are nothing but revelatory with:
Improved data usability and productivity enhancements for the business teams
Reusable architecture that natively integrates with AWS ecosystem
Real-time access to data with X2 faster data retrievals with minimal to no dependency on IT teams
Your data analytics capability on cloud can be value accretive not only with costs and process efficiencies, but also providing business users new ways to analyze data in-place using analytics tools or business intelligence platforms, and Trianz AFQ Connectors can enable all these much more. Schedule a demo of the Athena AFQ solution to learn more and get a free PoV.