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Spark Structured Streaming

Tale of Apache Spark

Reading Time: 6 minutes Data is being produced extensively in today’s world and it is going to be generated more rapidly in future. 90% of total data that is produced in the world is produced in last two years only and it is estimated that in 2020 world’s total data would reach 45 ZB and data generated each day would be enough that if we try to store it Continue Reading

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Using Vertica with Spark-Kafka: Write using Structured Streaming

Reading Time: 3 minutes In two previous blogs, we explored about Vertica and how it can be connected to Apache Spark. The first blog in this mini series was about reading data from Vertica using Spark and saving that data into Kafka. The next blog explained the reverse flow i.e. reading data from Kafka and writing data to Vertica but in a batch mode. i.e reading data from Kafka Continue Reading

Spark Streaming vs. Structured Streaming

Reading Time: 6 minutes Fan of Apache Spark? I am too. The reason is simple. Interesting APIs to work with, fast and distributed processing, unlike map-reduce no I/O overhead, fault tolerance and many more. With this much, you can do a lot in this world of Big data and Fast data. From “processing huge chunks of data” to “working on streaming data”, Spark works flawlessly in all. In this Continue Reading

Spark: RDD vs DataFrames

Reading Time: 3 minutes Spark SQL is a Spark module for structured data processing. Unlike the basic Spark RDD API, the interfaces provided by Spark SQL provide Spark with more information about the structure of both the data and the computation being performed. Internally, Spark SQL uses this extra information to perform extra optimizations.One use of Spark SQL is to execute SQL queries. When running SQL from within another Continue Reading

Structured Streaming: Philosophy behind it

Reading Time: 3 minutes In our previous blogs: Structured Streaming: What is it? & Structured Streaming: How it works? We got to know 2 major points about Structured Streaming – It is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. It treats the live data stream as a table that is being continuously appended/updated which allows us to express our streaming computation as Continue Reading

Structured Streaming: How it works?

Reading Time: 2 minutes In our previous blog post – Structured Streaming: What is it? we got to know that Structured Streaming is a fast, scalable, fault-tolerant, end-to-end, exactly-once stream processing API that helps users in building streaming applications. Now it’s time to learn  – How it works? So, in this blog post, we will look at the working of a structured stream via an example. So, let’s take a Continue Reading

Structured Streaming: What is it?

Reading Time: 3 minutes With the advent of streaming frameworks like Spark Streaming, Flink, Storm etc. developers stopped worrying about issues related to a streaming application, like – Fault Tolerance, i.e., zero data loss, Real-time processing of data, etc. and started focussing only on solving business challenges. The reason is, the frameworks (the ones mentioned above) provided inbuilt support for all of them. For example: In Spark Streaming, by just adding Continue Reading

KnolX: Understanding Spark Structured Streaming

Reading Time: < 1 minute Hello everyone, Knoldus organized a session on 05th January 2018. The topic was “Understanding Spark Structured Streaming”. Many people attended and enjoyed the session. In this blog post, I am going to share the slides & video of the session. Slides:

fetching data from different sources using Spark 2.1

What’s new in Apache Spark 2.2

Reading Time: 2 minutes Apache recently released a newer version of Spark i.e Apache Spark 2.2. The new version comes with new improvements as well as the addition of new functionalities. The major addition to this release is Structured Streaming. It has been marked as production ready and its experimental tag has been removed. Some of the high-level changes and improvements : Production ready Structured Streaming Expanding SQL functionalities New Continue Reading

Having Issue How To Order Streamed Dataframe ?

Reading Time: 3 minutes A few days ago, i have to perform aggregation on streaming dataframe. And the moment, i apply groupBy for aggregation, data gets shuffled. Now the situation arises how to maintain order? Yes, i can use orderBy with streaming dataframe using Spark Structured Streaming, but only in complete mode. There is no way of doing ordering of streaming data in append mode and update mode. I Continue Reading

Exploring Spark Structured Streaming

Reading Time: 6 minutes Hello Spark Enthusiasts, Streaming apps are growing more complex. And it is getting difficult to do with current distributed streaming engines. Why streaming is hard ? Streaming computations don’t run in isolation. Data arriving out of time order is a problem for batch-processed processing. Writing stream processing operations from scratch is not easy. Problem with DStreams: Processing with event-time: dealing with late data. Interoperate streaming Continue Reading

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