How To Read From HDFS & Persist In PostgreSQL Via Spark?
Reading Time: 4 minutes A Spark app that takes data from HDFS and persists in Vertica.
Apache Spark: Handle Corrupt/Bad Records
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
Amazon EMR
Reading Time: 3 minutes Businesses worldwide are discovering the power of new big data processing and analytics frameworks like Apache Hadoop and Apache Spark, but they are also discovering some of the challenges of operating these technologies in on-premises data lake environments. They may also have concerns about the future of their current distribution vendor. Common problems of on-premises big data environments include a lack of agility, excessive costs, Continue Reading
Data Lake – Build it in Phases
Reading Time: 3 minutes Data Lake – How to build a data lake and what are the phases involved in the same.
Apache Spark: Repartitioning v/s Coalesce
Reading Time: 3 minutes Does partitioning help you increase/decrease the Job Performance? Spark splits data into partitions and computation is done in parallel for each partition. It is very important to understand how data is partitioned and when you need to manually modify the partitioning to run spark applications efficiently. Now, diving into our main topic i.e Repartitioning v/s Coalesce What is Coalesce? The coalesce method reduces the number Continue Reading
Big Data Landscape explained
Reading Time: 5 minutes Big Data has now evolved into a buzz word and it seems everyone is either working on it or want to work on it. However, most of the people associate Big Data with some of the popular tool sets like Hadoop, Spark, NoSql databases like Hive, Cassandra , HBase etc. HDFS made Big Data popular as it gave us an option to distribute the data Continue Reading
Understanding how Spark runs on YARN with HDFS
Reading Time: 6 minutes This blog pertains to Apache SPARK and YARN (Yet Another Resource Negotiator), where we will understand how Spark runs on YARN with HDFS. So let’s get started. First, let’s see what Apache Spark is. The official definition of Apache Spark says that “Apache Spark™ is a unified analytics engine for large-scale data processing.” It is an in-memory computation processing engine where the data is kept Continue Reading
Deep Dive into Apache Spark Transformations and Action
Reading Time: 4 minutes In our previous blog of Apache Spark, we discussed a little about what Transformations & Actions are? Now we will get deeper into the topic and will understand what actually they are & how they play a vital role to work with Apache Spark? What is Spark RDD? Spark introduces the concept of an RDD (Resilient Distributed Dataset), an immutable fault-tolerant, distributed collection of objects Continue Reading
Spark – Actions and Transformations
Reading Time: 4 minutes Hey guys, welcome to series of spark blogs, this blog being the first blog in this series we would try to keep things as crisp as possible, so let’s get started. So I recently get to start learning spark about believe me and now it has made me inquisitive about it, for a brief introduction of spark, I would say that it is a pretty Continue Reading
Big Data Evolution: Migrating on-premise database to Hadoop
Reading Time: 4 minutes We are now generating massive volumes of data at an accelerated rate. To meet business needs, address changing market dynamics as well as improve decision-making, sophisticated analysis of this data from disparate sources is required. The challenge is how to capture, store and model these massive pools of data effectively in relational databases. Big data is not a fad. We are just at the beginning Continue Reading
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
Using Vertica with Spark-Kafka: Writing
Reading Time: 4 minutes In previous blog of this series, we took a glance over the basic definition of Spark and Vertica. We also did a code overview for reading data from Vertica using Spark as DataFrame and saving the data into Kafka. In this blog we will be doing the reverse flow i.e. working on reading the data from Kafka as a DataFrame and writing that DataFrame into Continue Reading
Tuning a Spark Application
Reading Time: 4 minutes Having trouble optimizing your Spark application? If yes, then this blog will surely guide you on how you can optimize it and what parameters should be tuned so that our spark application gives the best performance. Spark applications can cause a bottleneck due to resources such as CPU, memory, network etc. We need to tune our memory usage, data structures tuning, how RDDs need to Continue Reading