The whole point of gathering data is to get insight, and with the rapid growth in technology, big data is the new norm. “Big data” here refers to a large volume of exponentially growing datasets from multiple sources.
SQL has become synonymous with big data and is often seen as the developer and data professional’s choice to interact with data. As a result, big data tools and frameworks have also adopted SQL in their operational systems. But why?
Read on to find out if SQL can be used for big data and some tips for big data management and analysis.
Can SQL Be Used for Big Data?
The question “Can SQL be used for big data?” has generated a lot of debate.
Well, the short answer is “yes.” However, there’s a “but.”
The answer to this question depends on several factors, like the databases and the context in which they operate. But, regardless, SQL has a place in the big data landscape.
SQL is considered the de facto language by developers and data professionals to access and interactively query data from various sources. As a result, many organizations and big tech companies have heavily invested in it.
When SQL was developed, the intent was simple: a language that could interact, query, and manipulate data in your databases. While it’s pretty efficient and has excelled at this task, especially with relational database management systems, there are some bottlenecks or cases when NoSQL would be preferred—for example, when dealing with unstructured data.
However, this doesn’t make SQL obsolete.
What makes SQL so great in the big data landscape is its combination of strengths—from its wide adoption to its open-source roots, simplicity, security, reliability, data consistency, and relational nature.
In addition, a couple of everyday applications also utilize SQL to store, analyze, and process big data. For instance, most banking institutions keep transaction records in Oracle databases. So, its ability to handle big data has been proved.
Despite the concern that SQL won’t be able to manage unstructured data, most modern database systems accommodate SQL and SQL-like syntax because of their benefits.
So, when should one use SQL for big data? Let’s start there.
When to Use SQL for Big Data
You should use SQL when reliability, security, data validity, and consistency are business priorities.
It works best with relational databases, which work best with multi-row transactions and data with a fixed schema. However, this doesn’t imply that every SQL system is entirely relational.
A fixed schema requires a predefined, structured format before storing the data. This schema allows you to query and modify the data without distorting previously stored data, thus ensuring data consistency.
Additionally, the SQL database’s ACID transaction properties provide total consistency, integrity, predictability, and dependability of all operations—something most NoSQL databases lack. The ACID properties ensure that all operations within a transaction follow:
atomicity—It either executes every line in your transaction statement or none of it. This helps prevent data corruption and data loss.
consistency—It achieves this by accepting changes only to predefined tables to ensure integrity.
isolation—Requests from multiple users accessing the same databases don’t interfere with one another, even when requests happen simultaneously.
durability—It achieves this by saving all successfully executed transactions.
Though reliable, SQL often needs organized data. As a result, it’s not always the best choice for all business use cases.
When Not to Use SQL for Big Data
Relational database management systems (RDBMS) are not always the best when handling big data. Today, we have more data in more significant volumes than ever before, and much of it is unstructured. Unfortunately, that wasn’t really the focus of the conventional RDBMS.
NoSQL is helpful in this situation. Let’s take a look at some of SQL’s shortcomings.
The first is the vertical scalability nature of traditional SQL databases. An RDMBS adds extra horsepower to the system as data grows to ensure faster operation. While this works at first, one encounters a performance bottleneck at some point. On the other hand, NoSQL will increase the number of database servers, allowing for horizontal scaling and more effective load distribution.
You have to predefine a fixed-table schema. Considering the current data trend, a flexible data model that’s less schema-focused would have an edge.
An RDBMS requires a higher degree of normalization. However, this ensures and avoids instances of data duplication and redundancy.
Although most appear to be drawbacks, some strengthen SQL, and there are various ways to work around these inefficiencies. A schema is also necessary unless the intent is just to store data. Several NoSQL vendors implement SQL or SQL-like syntax interfaces to handle specific jobs that NoSQL databases cannot perform.
So, one would say keep SQL and incorporate scale-out architecture.
How to Use SQL for Big Data Management and Analysis
Here are some tips for working with big data using SQL, especially for big data management and analysis.
Ensure data consistency by labeling keys and views accordingly and discarding legacy columns and tables. Do this and watch how much easier your data maintenance checks get.
Don’t ignore the fundamental do’s and don’ts. Having descriptive names, knowing when to use uppercase and lowercase, and following the SQL execution order are a few of these. While a few wouldn’t technically impact performance, they make analysis much smoother.
Talking about performance, some notable tips are
improve data retrieval time by using indexing,
incorporate and write efficient indexable WHERE clauses,
use MERGE to reference the database, and
use SQL Wildcards more effectively.
It’s best to store dates as DateTime and UTC timezone to avoid time series analysis issues.
Consider partitioning your database to help with load performance.
Normalize your databases to eliminate redundant data. The more normalized your database gets, the closer it gets to an ACID-compliance state.
SQL Solutions for Big Data
Various use cases demand different solutions. While some tools and technologies, like SQLite, may not be able to support big data, there are some technologies built specifically for it. Some examples of such technologies are Google BigQuery, Presto, Apache Spark, Hive, Cloudera Impala, and SQream.
A couple of them are SQL-centric or use SQL-like syntax (HiveQL, Spark SQL, or SQL on Hadoop) with a unique extension to other programming languages and frameworks. However, SQream processes big data in SQL using GPUs and MPP-on-chip technology. With these, it’s possible to use SQL without sacrificing performance or scalability and manage data more effectively than conventional RDBMS.
Whatever technology you decide on, consider your data, pick the one that best suits your business use case, understand it, and make a plan for your tradeoffs.
Choosing an SQL Solution for Big Data
Choosing the right tool is by no means an easy undertaking. Thus, it’s essential to ensure your SQL solution checks your boxes. Of course, depending on your business data, your checklist might differ. However, here are a few things to consider before committing to one:
Does it support secondary indexes?
Can it handle unstructured data?
What’s the application’s algorithm for large joins?
What are the analytical and SQL capabilities?
What about its SQL query execution and optimization?
And, lastly, its latency requirements.
While there isn’t a “one size fits all,” especially when working with big data using SQL, understanding your SQL solution will be helpful.
So, read the documentation or book a call with the team.
Big Data and SQL
So, can SQL be used for big data? Absolutely yes.
It’s quite clear that SQL is crucial because NoSQL is an expansion, not a replacement. Numerous SQL solutions can handle big data. For example, SQream’s robust JOIN and GPU-acceleration features simplify big data consumption and analysis.
This post was written by Ifeanyi Benedict Iheagwara. Ifeanyi is a data analyst and Power Platform developer who is passionate about technical writing, contributing to open source organizations, and building communities. Ifeanyi writes about machine learning, data science, and DevOps, and enjoys contributing to open-source projects and the global ecosystem in any capacity.