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Big Data is one of the most valuable commodities in business today, but only if organizations have the power to analyze it and make it work for them.
The term "Big Data" represents a massive amount of structured and unstructured data from several different sources. As more and more companies find themselves in possession of Big Data, there's a greater need for tools that can extract useful insights from their monstrous reservoirs of information.
Data query engines are one of the most valuable tools in this category. In a nutshell, query engines allow companies to connect data from any source, any technology, or in any format and then query it with simple SQL commands.
In this high-level overview, we'll take a look at the power of data query engines, as well as provide a few tips for implementing them.
To make use of their Big Data, organizations need a way to query, merge, and join data seamlessly, but the challenge is the sheer amount of different data sources and formats.
Data is found in relational databases, CSV files, XML spreadsheets, text files, non-SQL databases, and several other sources, each of which has a completely different format and structures, making it extremely difficult to analyze.
The old classic solution is to upload all of this unstructured data to a single relational database, but this requires a lot of scripts and ETL (extract, transform, and load) programs to deal with the many different formats. Relational databases are also quite slow when it comes to processing data as they don't usually have the computing power to deal with many sources.
In order to extract any meaningful information from these data sources, companies need them to fall under a single common format, which is where data query engines come in. Query engines allow companies to connect data from different sources in different formats and different technologies and then query that data in the same way.
All query engines work with SQL, a data query language that is well-known and easy to learn. As a widely used and accessible query language, SQL is the defacto standard for commanding a system on how to display data. Query engines offer the standard SQL interface while hiding the complexity of the data storage configuration, making them extremely valuable and easy to use.
Data query engines are distributed in a way that allows organizations to process Big Data extremely quickly.
Relational databases are usually configured to one node, host, or server. Their performance is determined by how much memory or processing power they have access to. Increasing computational power to improve the performance of a relational database is known as vertical scalability, which is an expensive process.
In Big Data, there is a more powerful approach known as distributed computing, which involves implementing a cluster of computers or servers that work together to solve a problem. All data query engines are distributed based on this approach, mostly with a driver node in command of the computing power, a resource manager for administering work between nodes, and a group of worker nodes that perform the computations.
With this architecture, companies can get much better response times for queries than are possible with a simple relational database.
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As we've seen from the architecture examples above, installing a query engine can be challenging for some companies and the learning curve is slightly steeper than with relational query engines.
The configuration of clusters, driver nodes, and resource managers requires the specific technical expertise of data engineers. However, with a team of data experts handling the infrastructure and deployment in the back end, companies can focus on perfecting their SQL knowledge, performing the queries, and gathering insights from their data.
While SQL is a widely used query language, it requires a fair amount of training and experience to use most effectively. Most people can learn the basics of SQL in a few weeks but when looking for deeper insights and more accurate reporting, or trying to understand how to debug queries when they fail, it can take a few months to master.
Notebooks are a great tool for improving SQL queries because they allow people to auto-complete queries, add colors to syntax, enable live syntax validation and highlight error lines, making SQL even easier to learn. Notebooks also offer simple visualizations, and the ability to export results.
Any organization that owns a large amount of data will quickly see the advantages of using query engines.
They allow businesses to quickly and easily search their entire pool of data for insights without the need for advanced technical knowledge. With the right data experts covering the deployment and installation process, along with some basic knowledge of SQL, companies can begin analyzing and reporting on their structured and unstructured data within a relatively short amount of time.
Contact us today to learn how we can help your organization implement query engines and tap into the real value of your Big Data.