Continuously evaluating the efficiency of the available data processing tools enables professionals to choose only the best solutions for their projects. We are comparing BigQuery vs. Spark to answer some of the questions you may have about these two solutions. Read our article here.
Apache Spark and Google’s BigQuery are both often mentioned by experts when discussing effective data processing. But are those solutions the same? Are they used for the same purpose? In fact, it is not so simple. In our comparison of Spark vs. BigQuery, we’ll provide you with all the significant information you need to make an informed decision regarding your tech stack.
What is BigQuery?
Google itself defines BigQuery as a: “Serverless, highly scalable, and cost-effective multicloud data warehouse designed for business agility”. You surely know already, that going “serverless” is kind of a modern trend in business nowadays, as cloud-based solutions often prove to be cheaper, more scalable and more flexible than traditional ones.
So, in general, Google provides users with a platform for storing data efficiently in terms of cost and performance. What are the most important features of BigQuery?
- Google BigQuery comes with built-in integrations that you can use to build a data lake that will suit your individual needs.
- You get access to BigQuery Omni – a rather flexible, multicloud analytics tool. Using it, you can easily, safely and cost-effectively analyze data across many types of clouds (for example, AWS and Azure).
- The BigQuery BI Engine allows you to analyze large and complex datasets interactively with sub-second query response time and high concurrency. This service integrates with another useful tool – Data Studio, which you can leverage for data visualization.
- If your business requires advanced analytics, and you want to build machine learning models based on your data, you can also do it with BigQuery (specifically with BigQuery ML).
- There are companies that need to process data in real-time in order to ensure the highest quality of their service. If your organization is among them, you’ll be happy to learn that BigQuery’s exceptional streaming insertion API speed provides a good basis for real-time analytics.
What is Apache Spark?
Apache Spark, on the other hand, is a data processing framework often compared with Hadoop. It can be used for performing actions on very large data sets very fast. Besides that, it can be used for distributing data processing tasks across multiple machines (on its own or working together with additional distributed computing tools). These two features make it a popular solution in the world of Big Data and Machine Learning.
What else do you need to know about it?
- Apache Spark consists of two components: a driver (that converts the code into many tasks) and executors (that execute the tasks distributed across worker nodes).
- Its simplicity makes it an easy-to-use tool for most potential users (data scientists, as well as developers).
- Apache Spark also provides a library for applying ML-based techniques to data (Spark MLlib). You get a framework meant for developing machine learning pipelines for processing structured data. You can also use Apache Spark to train ML models with R programming language or Python.
- Similarly to BigQuery, Spark has some solutions for real-time or near real-time data processing. However, it may not achieve as good performance as some other available solutions for real-time data processing.
- Spark GraphX is an interesting solution that enables processing of graph structures.
Spark vs. BigQuery from Google – similarities
When comparing two tools, the first question is quite simple: are they the same type of solution? In considering BigQuery vs. Spark, we’ve sort of already answered this question – Apache Spark is a data processing framework and BigQuery is a cloud-based data warehouse, but…
In the case of Google’s platform, it is more complex than it sounds. It is not only a storage solution, as it comes with quite a few computing tools for processing data. In short, we can call it a data warehouse with additional data processing capabilities.
You have probably already realized that from all the features of both Spark and BigQuery, we’ve mostly chosen and described those that they have in common. In theory, they both enable users to efficiently process data, taking advantage of real-time processing and machine learning. There are in fact many things that these two solutions have in common.
The first significant similarity worth mentioning in our comparison is architecture. In BigQuery the query engine is called Dremel, and it is – obviously – a Google product. The feature that makes it similar to Apache is the change of the execution plan at runtime (just as with Spark’s Adaptive Query Execution). Another similarity is BigQuery’s Query Master, which plays the same role as the above-mentioned driver in Apache Spark.
There are some similarities in data processing as well – one of the most important examples is shuffle. BigQuery carries it out the same way as Apache Spark. If you are familiar with processes such as the bucket pruning or dynamic partition pruning typical for Apache Spark, you should also know that you can take advantage of them using BigQuery. This feature is only named differently in Google’s platform (“clustering”).
BigQuery vs. Spark – what are the main differences?
We’ve mentioned some common features of both Apache Spark and BIgQuery, but how do these solutions differ from each other?
In terms of performance, BigQuery seems to be significantly better than Apache Spark for processing both small and large datasets. Operative efficiency is most likely one of the major reasons why professionals choose Google’s platform over Spark.
As BigQuery is 100% serverless, there is no maintenance necessary on the user side. You simply have to enter your data, and you can start to work – practically no need to use any company resources to set it up. It is sort of ready-to-use. Apache Spark will have to be installed and configured by your team. It may not be a difficult task, but it consumes some of your experts’ time.
One of the main benefits of Apache Spark is that it is open-source. That means that as long as your organization respects the Apache Software Foundation’s software license and trademark policy, you can use it totally for free for commercial purposes. However, it is worth remembering that there will be additional underlying computing layer cost associated. Google BigQuery consists of two main components: storage and analysis. You have to pay for both. Fortunately, as with any other cloud-based solutions, you can use a pay-as-you-go model, which means that you pay only for those resources you actually use.
Which solution should you choose for your project?
Apache Spark has gained significant popularity among developers and data scientists, but it seems that BigQuery may win in the long run. Google’s serverless solution is evolving quickly. BigQuery Omni allows users to run queries on data stored in an external cloud platform. Its advantages surely compensate for its eventual weakness. Moreover, by picking BigQuery you can benefit from seamless integration with other useful tools from Google. Finally, it is highly possible that, thanks to its great performance, you will increase data processing efficiency in your company, hence reducing the costs of doing business in general.
If your company is looking for a modern solution to provide for all of your users a fast and responsive experience, don’t hesitate to contact us. We will help you face data management challenges and make the most out of your system.
Check out our blog for more details on Big Data:
- What is Big Query, and how can it support your analytics?
- Why is BigQuery replacing Hadoop for enterprise analytics?
- Optimizing Apache Spark