Spark Data Structures

Kevin Feasel

2017-07-31

Spark

Shubham Agarwal explains the difference between three Spark data structures:

DataFrame(DF) – 

DataFrame is an abstraction which gives a schema view of data. Which means it gives us a view of data as columns with column name and types info, We can think data in data frame like a table in the database.

Like RDD, execution in Dataframe too is lazy triggered.

Read on to learn more about Resilient Distributed Datasets, DataFrames, and DataSets.

Related Posts

Batch Consumption from Kafka with Spark

Swapnil Chougule shares a few tips on performing batch processing of a Kafka topic using Apache Spark: Spark as a compute engine is very widely accepted by most industries. Most of the old data platforms based on MapReduce jobs have been migrated to Spark-based jobs, and some are in the phase of migration. In short, […]

Read More

Securely Accessing External Resources From Databricks AWS

Itai Weiss shows how you can securely hit external data sources when using Databricks for AWS: For security purposes, Databricks Apache Spark clusters are deployed in an isolated VPC dedicated to Databricks within the customer’s account. In order to run their data workloads, there is a need to have secure connectivity between the Databricks Spark […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31