Spark Cassandra Training Bangalore – Learn from Experts with Hands On!
Spark Cassandra Training in Bangalore with Big Data
Spark Cassandra Training
Apache Spark is a cluster computing platform designed to be fast and general-purpose.
On the speed side, Spark extends the popular MapReduce model to efficiently support more types of computations, including interactive queries and stream processing. Speed is important in processing large datasets, as it means the difference between exploring data interactively and waiting minutes or hours. One of the main features Spark offers for speed is the ability to run computations in memory, but the system is also more efficient than MapReduce for complex applications running on disk. Spark Cassandra Training Bangalore .
Escape from Hadoop: with Apache Spark and Cassandra with the Spark Cassandra Connector What is Apache Spark, why use it over Cassandra without Hadoop and how. What is Spark Streaming, and how to use it with Apache Cassandra via the Spark Cassandra Connector.
Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity In Massive Scale with Apache: Spark, Kafka, Cassandra and Akka, Streaming Big Data: Delivering Meaning In Near-Real Time At High Velocity At Massive Scale with Apache Spark, Apache Kafka, Apache Cassandra, Akka and the Spark Cassandra Connector. Why this pairing of technologies and How easy it is to implement.
Interactive Analytics with Spark and Cassandra , learn to take your analytics to the next level by using Apache Spark to accelerate complex interactive analytics using your Apache Cassandra data. Includes an introduction to Spark as well as how to read Cassandra tables in Spark
For the real-time path the obvious processing solution is Spark Streaming (so we have a simpler code base) running on Mesos with Kafka to feed data in and with Cassandra to store the results. You now have a so-called SMACK stack (Spark Mesos Apache Cassandra Kafka) for data processing which the Mesos folks call Mesosphere Infinity for some reason (aka marketing).
The last bit of a data architecture is the SQL engine. Traditionally this was Hive but we all know Hive is slow. While there are several open-source solutions out there that improve on good old Hive (Impala, Spark SQL) in the end we decided on AWS Redshift. It’s a column-oriented SQL-based data warehouse with PostgreSQL interface which fulfils most of the data analysis and data science needs while being reasonably fast and relatively easy to maintain with few people.