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Apache Spark

Apache Spark

Brief Introduction to Apache Spark

Studio-Scala,

Reading Time: 3 minutes What is Apache Spark: Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs. It supports various programming languages like Scala, Python, Java, and R. Spark Architecture: Uses of Apache Spark: It is used for- Applications of Data processing Batch processing Processing Structured data Machine learning Process graph data Processing Streaming data Features of Continue Reading

Writing Unit Test for Apache Spark using Memory Streams

Apache Spark, scalatest, Studio-Scala, , , ,

Reading Time: 2 minutes In this post, we are going to look into how we can leverage apache spark’s memory streams for Unit testing What is it ? Apache spark’s memory streams is a concrete streaming source of memory data source that supports reading in Micro-Batch Stream Processing. Lets jump into it We will be using a memory stream writing some test data in memory as a stream. We Continue Reading

Creating Data Pipeline with Spark streaming, Kafka and Cassandra

Apache Kafka, Apache Spark, Big Data and Fast Data, Cassandra, MessagesAPI, Spark, Streaming, Studio-Scala, , , ,

Reading Time: 3 minutes Hi Folks!! In this blog, we are going to learn how we can integrate Spark Structured Streaming with Kafka and Cassandra to build a simple data pipeline. Spark Structured Streaming is a component of Apache Spark framework that enables scalable, high throughput, fault tolerant processing of data streams.Apache Kafka is a scalable, high performance, low latency platform that allows reading and writing streams of data Continue Reading

Spark: Streaming Datasets

Apache Spark, Big Data and Fast Data, Spark, Streaming, Studio-Scala, , , , , ,

Reading Time: 3 minutes Spark providing us a high-level API – Dataset, which makes it easy to get type safety and securely perform manipulation in a distributed and a local environment without code changes. Also, spark structured streaming, a high-level API for stream processing allows us to stream a particular Dataset which is nothing but a type-safe structured streams. In this blog, we will see how we can create Continue Reading

Delta Lake: Schema Enforcement & Evolution

Apache Spark, Big Data and Fast Data, Java, Spark, , , ,

Reading Time: 4 minutes Nowadays data is constantly evolving and changing. As well as the business problems and requirements are evolving, the shape or the structure of the data is also changing. When that happens, we want to be in control of how the data or schema changes. But how we can achieve this? Delta Lake has good ways to control how schema changes. With Delta Lake, users have Continue Reading

fetching data from different sources using Spark 2.1

Spark: createDataFrame() vs toDF()

Apache Spark, Big Data and Fast Data, Spark, Studio-Scala, , , , , ,

Reading Time: 2 minutes There are two different ways to create a Dataframe in Spark. First, using toDF() and second is using createDataFrame(). In this blog we will see how we can create Dataframe using these two methods and what’s the exact difference between them. toDF() toDF() method provides a very concise way to create a Dataframe. This method can be applied to a sequence of objects. To access Continue Reading

Streaming from Kafka to PostgreSQL through Spark Structured Streaming

Apache Kafka, Apache Spark, Studio-Scala, , , , ,

Reading Time: 3 minutes Hello everyone, in this blog we are going to learn how to do a structured streaming in spark with kafka and postgresql in our local system. We will be doing all this using scala so without any furthur pause, lets begin. Setting up the necessities first: Dependencies Set up the required dependencies for scala, spark, kafka and postgresql. 2. PostgreSQL setup Lets start fresh by Continue Reading

Apache Spark: Handle Corrupt/Bad Records

Analytics, Apache Spark, AWS, Big Data and Fast Data, Database, HDFS, ML, AI and Data Engineering, Spark, SQL, Studio-Scala, , ,

Reading Time: 3 minutes Most of the time writing ETL jobs becomes very expensive when it comes to handling corrupt records. And in such cases, ETL pipelines need a good solution to handle corrupted records. Because, larger the ETL pipeline is, the more complex it becomes to handle such bad records in between. Corrupt data includes: Missing information Incomplete information Schema mismatch Differing formats or data types Apache Spark: Continue Reading

Apache Spark: Tricks to Increase Job Performance

Analytics, Apache Spark, Big Data and Fast Data, Database, ML, AI and Data Engineering, NoSql, Spark, SQL, Streaming, Streaming Solutions, Studio-Scala, , , , , , , , ,

Reading Time: 2 minutes Apache Spark is quickly adopting the Real-world and most of the companies like Uber are using it in their production. Spark is gaining its popularity in the market as it also provides you with the feature of developing Streaming Applications and doing Machine Learning, which helps companies get better results in their production along with proper analysis using Spark. Although companies are using Spark in Continue Reading

Spark: ACID Transaction with Delta Lake

Apache Spark, Big Data and Fast Data, Java, NoSql, Spark, Studio-Scala, , , , , ,

Reading Time: 3 minutes Spark doesn’t provide some of the most essential features of a reliable data processing system such as Atomic APIs and ACID transactions as discussed in the blog Spark: ACID compliant or not. Spark welcomes a solution to the problem by working with Delta Lake. Delta Lake plays an intermediary service between Apache Spark and the storage system. Instead of directly interacting with the storage layer, Continue Reading

Time Travel: Data versioning in Delta Lake

Analytics, Apache Spark, Big Data and Fast Data, Java, Spark, Studio-Scala, , , , , , , , ,

Reading Time: 3 minutes In today’s Big Data world, we process large amounts of data continuously and store the resulting data into data lake. This keeps changing the state of the data lake. But, sometimes we would like to access a historical version of our data. This requires versioning of data. Such kinds of data management simplifies our data pipeline by making it easy for professionals or organizations to Continue Reading

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