Ever thought about being able to look in all the rooms in your house at once for the car keys that you seem to have lost? Now imagine instantaneously looking in every book in a library for an answer — regardless of the language of the book. Federated queries make this possible, says Ganeshan Venkateshwaran, President, Trianz
Organizations often find it challenging to access the correct data at the right time quickly. Data is often stored in several database and storage systems such as relational databases (MySQL, SQL Server, Postgres) or object storage systems (S3, HDFS). With federated queries and connectors, you can instantly access all the data from disparate sources.
What are federated queries?
With a federated query, what is being “federated” or brought together are the sources of information that can answer the question. For instance, you could retrieve a list of all books in US libraries written by people born in San Francisco or generate a statement correlating a customer’s credit ratings with their age and gender.
In both examples, data resides in two or more data sources. Traditional analytics engines were able to run only one search from one seed at a time. Adding other parameters from other sources wasn’t possible.
Facilitating access to data from multiple sources in a single query and quickly is revolutionary about federated queries.
But the concept of federated queries itself isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.
How federated queries benefit analytics
As organizations store more data, they will have a more significant amount of inaccessible information. They will need excellent aggregation and transformation capabilities to leverage this vast repository of data to improve their decision-making process. They will also need to bring it together and translate it into a standard form for analytics.
- Federated queries can significantly aid in the process. The benefits are immense compared to the traditional querying approaches of other database solutions:
- There is no need for users to remember credentials or log into individual databases as everything is centralized within the federated query service, enabling unified access to data across all source types and IT environments.
- When departments, such as sales, marketing, and operations, have the same access to data for queries and reports, it will eliminate organizational silos. This enables a comprehensive view of the sales funnel, which the organizations can use to improve lead generation.
- Federated queries make it easier for data scientists and analysts to analyze data. Traditional ETL tools were geared more toward developers and coders who understood database language.
- Federated queries are usually optimized before execution, enabling hundreds of user queries to be load-balanced and de-duplicated in real-time. This leads to higher throughput and lowers costs when using advanced analytics or business intelligence tools and promotes data-driven decision-making.
The most significant advantage is that the users don’t need to know each database’s specific query or data language. Automated Data Definition Language (DDL) conversion in federated queries allows anyone to perform queries on all data sources.
Role of federated query connectors
Federated queries allow you to query the data from multiple sources. But every interactive query service uses data source connectors that run on a serverless computing platform to run federated queries.
A data source connector is a piece of code that acts as a translator between your target data source and query service, from Google, Amazon, or Microsoft. When a query is submitted against a data source, your query service invokes the corresponding connector to identify parts of the tables that need to be read and pushes down the information.
For instance, in my firm, we have built Athena Rapid Analytics to break data barriers by providing the ability to connect and query databases across on-premises and other public cloud environments. Athena Rapid Analytics is our federated data solution that supports SQL, Java Database Connectivity (JDBC), and Open Database Connectivity (ODBC) across public/private cloud, hybrid-cloud, and on-premises IT infrastructure types.
You can use the custom Athena query federation connectors to integrate with Athena SDK for a seamless query authoring experience and implement best security practices as defined by AWS. The connectors use existing AWS identity and access management policies, cryptographic data protection for credential management, and cloud watch for audit management.
Harness the power of your data
Analytics is essential for driving business success. Aggregating data into one location is the key to real-time, effective analytics. And working with serverless interactive query service means there’s zero infrastructures to manage with seamless collaboration and secure encryption capabilities. You’ll also get results within seconds at optimized cost and with zero training. The time is now to decide what is better suited to your unique needs!