What do you need to know?
Prepare the Data
Use Extract, Transfer, Load (ETL) processes to prepare data for queries.
Import data from a MySQL database into HDFS using Sqoop
Export data to a MySQL database from HDFS using Sqoop
Move data between tables in the metastore
Transform values, columns, or file formats of incoming data before analysis
Provide Structure to the Data
Use Data Definition Language (DDL) statements to create or alter structures in the metastore for use by Hive and Impala.
Create tables using a variety of data types, delimiters, and file formats
Create new tables using existing tables to define the schema
Improve query performance by creating partitioned tables in the metastore
Alter tables to modify existing schema
Create views in order to simplify queries
Use Query Language (QL) statements in Hive and Impala to analyze data on the cluster.
Prepare reports using SELECT commands including unions and subqueries
Calculate aggregate statistics, such as sums and averages, during a query
Create queries against multiple data sources by using join commands
Transform the output format of queries by using built-in functions
- Perform queries across a group of rows using windowing functions
What should you expect?
You are given ten to twelve customer problems with a unique large data set, a CDH cluster, and 120 minutes. For each problem, you must implement a technical solution with a high degree of precision that meets all the requirements. You may use any tool or combination of tools on the cluster (see list below) -- you get to pick the tool(s) that are right for the job. You must possess enough knowledge to analyze the problem and arrive at an optimal approach given the time allowed. You need to know what you should do and then do it on a live cluster, including a time limit and while being watched by a proctor.
Number of Questions: 10–12 performance-based (hands-on) tasks on CDH5 cluster. See below for full cluster configuration
Time Limit: 120 minutes
Passing Score: 70%
Who is this for?
Candidates for CCA Data Analyst can be SQL devlopers, data analysts, business intelligence specialists, developers, system architects, and database administrators.There are no prerequisites.
What is the best way to prepare?
The CCA Data Analyst exam was created to identify talented SQL developers looking to stand out and be recognized by employers looking for these skills. It is recommended that those looking to achieve this certification start by taking Cloudera's Data Analyst training course, which has the same objectives as the exam.
Exam delivery and cluster information
CCA159 is a hands-on, practical exam using Cloudera technologies. Each user is given their own CDH5 (currently 5.8) cluster pre-loaded with Spark, Impala, Crunch, Hive, Pig, Sqoop, Kafka, Flume, Kite, Hue, Oozie, DataFu, and many others (See a full list). In addition the cluster also comes with Python (2.6, 2.7, and 3.4), Perl 5.10, Elephant Bird, Cascading 2.6, Brickhouse, Hive Swarm, Scala 2.11, Scalding, IDEA, Sublime, Eclipse, and NetBeans.
Documentation Available online during the exam
Only the documentation, links, and resources listed above are accessible during the exam. All other websites, including Google/search functionality is disabled. You may not use notes or other exam aids.