Why you should get into Apache Spark and Kafka
Learn more about these two company favorites
Companies love to build their data platforms on top of Apache Kafka and Apache Spark. And they do so for some very good reasons. Reason enough for you as data engineers to get into these two tools asap. In the following I am going to explain why.
Fast, scalable, durable: Apache Kafka
Kafka is a distributed messaging system and known to be a fast, scalable, and durable alternative to existing solutions on the market.
It can be scaled quickly and easily without incurring any downtime. Furthermore, Kafka is able to support a huge quantity of consumers and hold and replicate tremendous amounts of data without incurring much at all in the way of overhead. It also automatically balances consumers in the event of failure. That means that it’s more reliable than similar messaging services available.
What makes Kafka such a durable messaging system is that it also persists the messages on the disks, which provides intra-cluster replication. And that’s not all! Apache Kafka delivers high throughput for both publishing and subscribing, utilizing disk structures that are capable of offering constant levels of performance, even when dealing with many terabytes of stored messages.
And when it comes to building data platforms and pipelines, Kafka is the gold standard for setting up a message queue. In general, the main thing with building data platforms is to keep the whole process as efficient as possible so that everything works in parallel and you don’t have any bottlenecks. And that’s what Kafka is perfect for: supporting efficient and scalable distributed processing.
Efficient and fast: Apache Spark
Spark is a lightning-fast unified analytics engine for big data and machine learning with a massive open-source community behind it, being known for its efficiency it offers to developers.
You can do the actual processing of the data with Spark on a large scale. It is a great tool for in memory processing for batch and stream processing, which makes it a booster for any business, helping to foster its growth.
Moreover, with Spark you can generate analytics reports in a better and faster way and handle multiple petabytes of clustered data of more than 8000 nodes at a time because of its low-latency in-memory data processing capability. It carries easy-to-use APIs for operating on large datasets and offers over 80 high-level operators that make it easy to build parallel apps.
And no, that’s not all. Spark also supports machine learning (ML), graph algorithms, SQL queries and many languages for code writing and offers well-built libraries for graph analytics algorithms and ML.
So no wonder that Spark developers are so in-demand that companies are offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark.
You see, Apache Kafka and Spark are more than worthy to get into and work with as a data engineer. And companies will hire you with a kiss on the hand when you present them with your well-trained skills with both of these tools.
Building up your knowledge
Already hooked and want to get into it right away? Then check out my Academy courses Apache Kafka Fundamentals and Learning Apache Spark.
You are already familiar with the fundamentals and want to expand your knowledge and skills in a hands-on project? Then my Document Streaming course is just the right thing for you. Here you work with Spark, Kafka and some other great tools you should know as a data engineer.
Are you looking for more information and content on Data Engineering? Then check out my other blog posts, videos and more on Medium, YouTube and LinkedIn!
