Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.
Hadoop can handle all types of data from disparate systems: structured, unstructured, log files, pictures, audio files, communications records, email– just about anything you can think of, regardless of its native format. Even when different types of data have been stored in unrelated systems, you can dump it all into your Hadoop cluster with no prior need for a schema. In other words, you don’t need to know how you intend to query your data before you store it; Hadoop lets you decide later and over time can reveal questions you never even thought to ask.
By making all of your data useable, not just what’s in your databases, Hadoop lets you see relationships that were hidden before and reveal answers that have always been just out of reach. You can start making more decisions based on hard data instead of hunches and look at complete data sets, not just samples.
- Cloudera Administrator Training for Apache Hadoop (CATAH)
Cloudera Training for Apache HBase (CAHB)
- Cloudera Developer Training for MapReduce (CDTMR)
- Cloudera Designing & Building Big Data Applications (CDBBDA)
- Cloudera Developer Training for Apache Spark (CDTAS)
- Cloudera Developer Training for Spark and Hadoop (CDTSH1)
- Cloudera Search Training (CST)
Data Analyst Training
Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH)
- Data Science at Scale using Spark and Hadoop (DSSH)