SQL is usually taken for granted. Structured Query Language (SQL) was originally created in 1974 and is one of the longest-standing witnesses of breakthroughs and progresses in database technology. Certified by both ANSI and ISO, it is the standard database query language that powers many well-known database applications on the internet today.
Where is SQL now?
For the past 5 years, SQL has been ranked among the top 2 of the most popular programming languages by Stack Overflow Developer Surveys. Coding Dojo also lists SQL as the most in-demand programming language in 2017. And SQL is technically not even a programming language! However, despite such popularity among professionals, SQL’s steady and consistent presence tends to blend itself quietly into the backdrop of the world of big data while the rest of us chase after new hypes.
The past decade saw the rise of Hadoop and NoSQL, which complement tasks and functions that SQL was not designed for. While these developments are very important for the course of progress in big data, some people mistakenly confused NoSQL for “No SQL” and claim that SQL is dead or no longer relevant. But with the growing popularity of cloud-based databases and the increasing number of SQL interfaces among Hadoop and NoSQL projects, it is clear now that the ever-evolving SQL isn’t going anywhere anytime soon.
When Mode and Data Virtuality discovered each other, we noticed our shared commonality right away: Mode and Data Virtuality both believe in the philosophy and technology behind SQL. In fact, we count on SQL in the era of big data.
A powerful SQL based BI stack with Mode + DataVirtuality
Mode Analytics is a collaborative platform for data-informed businesses, powered by SQL. Whether you reach for SQL and Python or prefer drag-and-drop exploration, Mode helps everyone to find answers in data. It is very versatile and you can use it for ad-hoc analysis, interactive reporting, data visualization, and dashboards.
Data Virtuality is a next-generation data integration platform that accesses, models, and moves data for analytics and process automation purposes, powered by SQL. Data Virtuality can analyze data from any database with over 200 connectors. Data Virtuality sets up your data infrastructure in under 5 minutes, accesses data silos, and uses SQL to query raw data for insights. The Data Virtuality SQL processor even works for Hadoop and NoSQL databases that do not support SQL natively.
So why do both Data Virtuality and Mode choose SQL as an essential part of their products? Here are some of the advantages of SQL:
- SQL is semantically simple and intuitive to understand
- SQL is easy-to-learn but powerful when utilized
- SQL queries retrieve large amounts of data from a database quickly and efficiently
- SQL databases use an industry standard adopted by ANSI and ISO for relational models
- SQL is popular with adopters of open source languages like Python and R
Because of these advantages, SQL is everywhere. Even the disruptive Hadoop and NoSQL products are now actively adding SQL-like syntax for their query and analysis languages, such as HiveQL (for Hadoop) and UnQL (for NoSQL). These recent trends demonstrate SQL’s strengths and importance in the age of big data. SQL’s ease-of-use, power, and flexibility is exactly why we choose it for our products.
The shared choice of SQL also makes the combination of Data Virtuality and Mode a powerful tool set for data-driven businesses. In the backend, Data Virtuality can connect to any data source with its various connectors and to any data warehouses, such as PostgreSQL, Amazon Redshift, MySQL, SQL Server, and BigQuery for data integration. On the frontend, Data Virtuality connects to the powerful Mode and allows its users to query data directly through Data Virtuality with SQL commands and perform analysis to explore insights.
As it combines the best features of both ELT and data virtualization, Data Virtuality integrates data from multiple sources into one virtual layer without the need to physically relocate the data storage, and executes queries instantly. Furthermore, it optimizes distributed queries while eliminating bottlenecks by intelligently creating and managing data structures in data warehouses, such as PostgreSQL, Amazon Redshift, MySQL, SQL Server, BigQuery, Snowflake and many more to keep the data up-to-date.
With Data Virtuality doing the magic behind the scenes, Mode users can now easily connect to any data source, from Twitter to MongoDB, with Data Virtuality and then query data for analysis using SQL. Leave the heavy lifting to Data Virtuality and just focus on enjoying the data analysis and exploration with Mode.
CHOOSE YOUR USE CASE: INTEGRATE YOUR DATA AND RUN AD-HOC QUERIES ON IT
You could simply bring all your data sources together and then query them ad-hoc in just a couple of clicks:
In our example the BI Stack would be as follows.
- Google Analytics
- Adobe Analytics
Data integration platform:
- Data Virtuality
… and we can also handle your specific use case. Just by using SQL and a few clicks.
The combination of Data Virtuality and Mode provides you with versatile front-end data analysis and powerful back-end data integration, powered by one language: SQL. NoSQL does not mean “No SQL” but simply that it’s “Not SQL” and it borrows powerful features from SQL to tap the insights of unstructured data. At Data Virtuality and Mode, we truly believe SQL has stood the test of time and continues to evolve flexibly with technological progress. SQL doesn’t just belong to the past, but it also unlocks the future of big data.
Do you share our passion for SQL? Are you curious about Data Virtuality’s products and solutions in combination with Mode? See a list of all data sources that you can get into Mode right here.