Extracting Business Value From Big Data With Pig and Hive
About Individual Course:
With Big data training, you help you to assemble and store the data in Hadoop for secure storage purpose, conclude value with use of Hive and Spark SQL, access the data which was inaccessible before and alter the data using Pig. It will further help you in increasing efficiency by quickly starting to deduce business value for competitive benefit and refraining low-level Java coding feature of MapReduce.
Transfer data automatically using in Sqoop and Flume in Hadoop Storage.
Perform analysis with use of Hive and Pig for query multiple data set.
To avail competitive advantage, deploy difficult data set stored in Hadoop.
Perform immediate measures on Hadoop by Tez and Spark SQL.
Use Pig to filter data by ETL (Extract-Transform-Load)
Basics of SQL and databases.
Virtual Instructed-Led Outline
The Hadoop Ecosystem
- Hadoop overview
- Surveying the Hadoop components
- Defining the Hadoop architecture
Exploring HDFS and MapReduce
Storing data in HDFS
- Achieving reliable and secure storage
- Monitoring storage metrics
- Controlling HDFS from the Command Line
Parallel processing with MapReduce
- Detailing the MapReduce approach
- Transferring algorithms not data
- Dissecting the key stages of a MapReduce job
Automating data transfer
- Facilitating data Ingress and Egress
- Aggregating data with Flume
- Configuring data fan in and fan out
- Moving relational data with Sqoop
Executing Data Flows with Pig
Describing characteristics of Apache Pig
- Contrasting Pig with MapReduce
- Identifying Pig use cases
- Pinpointing key Pig configurations
Structuring unstructured data
- Representing data in Pig's data model
- Running Pig Latin commands at the Grunt Shell
- Expressing transformations in Pig Latin Syntax
- Invoking Load and Store functions
Performing ETL with Pig
Transforming data with Relational Operators
- Creating new relations with joins
- Reducing data size by sampling
- Extending Pig with user?defined functions
Filtering data with Pig
- Consolidating data sets with unions
- Partitioning data sets with splits
- Injecting parameters into Pig scripts
Manipulating Data with Hive
Leveraging business advantages of Hive
- Factoring Hive into components
- Imposing structure on data with Hive
Organizing data in Hive Data Warehouse
- Creating Hive databases and tables
- Contrasting available data types in Hive
- Loading and storing data efficiently with SerDes
Designing data layout for maximum performance
- Populating tables from queries
- Partitioning Hive Tables for optimal queries
- Composing HiveQL queries
Extracting Business Value with HiveQL
Performing joins on unstructured data
- Distinguishing joins available in Hive
- Optimizing join structure for performance
Pushing HiveQL to the limit
- Sorting, distributing and clustering data
- Reducing query complexity with views
- Improving query performance with indexes
Deploying Hive in production
- Designing Hive schemas
- Setting up data compression
- Debugging Hive scripts
Streamlining storage management with HCatalog
- Unifying the data view with HCatalog
- Leveraging HCatalog to access the Hive metastore
- Communicating via the HCatalog interfaces
- Populating a Hive table from Pig
Interacting with Hadoop Data in Real Time
- Reducing data access times with Spark SQL
- Querying Hive data with Spark SQL
|Learning Style||Virtual Classroom|
|Course Duration||4 Days|
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