You must have heard of Ksolves as a software development company. And, yes, we are best at it. But, that is not all we do. Sometimes along with developing software, we talk to companies who are in the early stages of building data infrastructure. We talk about technologies to pick, their needs and what is best for them. Recently, one of our clients has asked us a question- Apache Spark vs. Amazon Redshift: what to choose? Is it good to use Spark or should they be using Redshift?
Spark and Redshift are two completely different technologies but, often organizations are confused between both of them. It is not about ‘what to use’? but more about, ‘when to use what’?
In this article, we will discuss in detail about the two most advanced technologies and provide you with a decision-making framework so that you can choose what’s best for your organization.
Apache Spark streaming platform is an open-source data processing engine. Spark lets you process batch and streaming workloads in real-time. It is written in Java, Scala, Python and R and uses pre-built libraries for building applications.
It is a fast, easy and scalable platform which speeds up the development and makes applications more portable and run faster.
Amazon Redshift is a fully managed analytical database which performs operations like building a central data warehouse, running big and complex analytical queries with SQL and passing the result to the dashboard.
Raw data flows into Redshift where it is processed and transformed. Redshift is managed by Amazon and helps you in analyzing bigger and more complex datasets.
Apache Spark vs. Amazon Redshift
Let’s have a look at Apache Spark vs. Amazon Redshift on the basis if what they are-
What excites people about Spark
- Spark is fast as it distributes data across a cluster and then parallely processes that data. It processes data in memory instead of shuffling things out of disk.
- Spark is easy as it lets you write applications with fewer codes. Also, scala and R are attractive languages.
- Spark has pre-built libraries which makes it extensible.
What excites people about Redshift
- Redshift is fast as it has a massive parallel processing architecture for distributing and parallelizing queries. Redshift also allows you to process high query.
- Redshift is said to be easy as it can take structured, unstructured as well as semi-structured data.
- Redshift is less expensive as it can store data at price points.
Apache Spark vs. Amazon Redshift: Difference in architecture
You can build an application with Spark and use Redshift as both data source and destination.
The major difference between Spark and Redshift is their way of processing data and the time they take to do it.
With Apache Spark you can do real-time streaming while Redshift allows you to do near real-time batch operations.
One such use case is fraud detection. Apache Spark lets you build an app that can detect fraud in real-time but, because of the near real-time characteristics, Redshift is not a good fit here.
Apache Spark vs. Amazon Redshift: Difference in data engineering
With new advances in business engineering and data warehouse, data engineering is on the rise. Data engineering is the system that unites both Spark and Redshift.
We are now seeing more and more code going into data warehousing. The code allows you to monitor data pipeline including the data transformation and you have to ingest data from Apache Spark. The latest trend suggests that SQL is not enough any more and one must have the knowledge of writing code.
In this Apache Spark vs Amazon Redshift debate we have concluded that Spark is a better option than Spark as it improves speed and performance of applications, used for real-time stream processing.
Ksolves is here to help you understand what is best for your business. Being one of the leading Apache Spark development company, our highly experienced Apache Spark developers are equipped with most advanced technologies that can help you with all your big data requirements. Leverage our Spark consulting Services and take your business to new heights.
If you need any further information on Apache Spark write to us in the comment section or give us a call write away!
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