Comprehensive Course on Hadoop Analytic Tool : Apache Hive

Define big data as a massive volume of structured, unstructured, and semi-structured data that exceeds traditional processing, and explore how it enables faster, smarter business decisions.

Explore Apache Hadoop's distributed processing framework for large data sets. Learn its fault-tolerant HDFS and MapReduce workflow managed by a master coordinating jobs across a cluster.

Explore HDFS in Hadoop, a distributed file system designed for high throughput on low-cost hardware, using block storage, replication, and leader-driven block management to ensure availability.

Explore how the Hadoop data processing framework schedules and runs map and reduce jobs across a cluster, managing input, output, and file system storage for scalable analytics.

Explore Hive use cases at Twitter, highlighting how large data loads exceed traditional RDBMS capabilities. Understand partitioned and bucketed tables and Hive language to enable scalable analytics.

Explore how Hive enables analyzing large datasets on Hadoop using a sql-like language to drive computation and support relational-style queries on structured data.

Discover how Hive supports batch processing, partitions, and queries that filter with where clauses, enabling storage of query results while optimizing for partitioning to boost performance.

Explore Hive architecture, including the user interface, driver, compiler, and execution engine, and learn how the metastore stores table structures and partitions and enables query optimization and execution.

Unpack Hive data types, from primitive to complex, covering integers, decimals, double precision, strings, binary, date, timestamp, and arrays, with practical access and null handling.

Explore HiveQL as the data definition and query language for Apache Hive, covering ddl commands, create and drop syntax, dialect variations, and limitations like no full transactional support.

Explore Hive DDL on databases by creating and using databases, describing their properties, and managing tables with describe commands and extended properties, including existence checks.

Explain DDL on tables in Apache Hive by creating tables with if not exists, defining schema (name string, salary float, address string), and using show tables and describe to inspect.

Explore how to create and manage different Hive tables, including managed and external tables, and use partitions to optimize storage and query performance.

Continue exploring Hive tables by examining how to work with data partitions and related concepts, presenting practical details from the lecture.

Explore advanced Hive ddl operations, including drop table if exists, create table with not exists, rename and alter columns, manage partitions, and add or replace columns in Hive tables.

Learn to set Hive table properties with alter table, including storage properties and file format, and manage date-based partitions while handling offline enablement.

Explore the five file formats in hive and how storage formats encode data; customize a table from text to sequence file or RCFile with an employee example.

Learn how to load data into Hive tables using DML, create tables with defined schemas, and manage partitioned data for efficient querying.

Learn to manage outputs in Hive by creating tables with dynamic and static partitions, loading data, and configuring dynamic partitioning in strict and non-strict modes.

Create a table from a select clause to load a subset of data, and explore exporting data and organizing directories and files in hive.

Explore Hive ql queries by building select statements with from clauses, where conditions, and aggregates; learn to create tables, insert data, and apply partitioning and grouping in Hive.

Explore Hive operators and functions, from arithmetic operations like addition and division to bitwise, comparison, and regex and like operators, with practical examples and select statements.

Explore Hive's math and string functions, including ceiling, square root, power, abs, and pi, plus aggregates like count, min, max, avg, sum, and substring, upper, lower, trim, length.

Explore Hive clauses, focusing on the where clause and like predicate to filter data with begins with, ends with, contains, and wildcard pattern matching.

Learn Hive clauses, including nested select, two like clauses, and case expressions, then group by with having and order by, sort by, and limit applied to salary and federal taxes.

Explore how joins in Hive connect tables, including left and right and outer joins, with customers and orders examples showing matched and unmatched rows and join conditions.

Explore how Apache Hive enables querying large datasets with a wordcount example: create a database and table, load text data, and count words.

Explore Hive views and the Hive language, learn how views save and present queries without extra storage, and see examples using employee data with select statements.

Explore Hive views, including creating and dropping views with unique names and optional external locations, and restrict sensitive data via department-based queries using employee examples.

Explore how Hive uses distribution by and cluster by to balance reducers, control sorting, and how cast and union operate to transform data across queries.

Learn how Hive indexing speeds queries by creating and using indexes on table columns, explore partitioning implications, and manage index lifecycle with creation, rebuilding, and dropping.

Learn indexing with additional properties by creating an index, assigning it to a specific day or date, and naming the index for targeted data retrieval.

Explore how Hive tunes queries by translating declarative queries into jobs, using explain and explain extended to reveal the abstract syntax tree and stage dependencies for memory-aware join optimization.

Execute Hive queries in local, in-process, and multi-stage modes, configure local mode and the job tracker, and manage partitions, limits, and ordering to optimize performance.

Discover how Hadoop tuning parameters affect high performance, including when dynamic partitioning and partition counts help or hinder tasks, and evaluate trade-offs in speculative execution.

Explore how Hive compression reduces read and transfer times by compressing input data, output data, and stored history, boosting performance while managing storage and network transfer.

Explore high compression codecs in Hive, compare supported codecs and file extensions, and evaluate trade-offs between speed, disk space, and decompression efficiency.

Explore sequence files in the Hadoop Hive workflow, uncovering how records are stored in blocks, compressed for space efficiency, and read from the beginning to access data.

Learn how to enable compression in Hive queries, see how compression shrinks data during shuffles, and perform decompression to obtain the final output.

Explore Hive functions, including built-in and user defined ones, and learn to discover, describe, and apply them to perform specific tasks in queries.

Explore standard and aggregate Apache Hive functions, including mathematical and string operations, plus calling user-defined and table-generating functions that explode inputs into outputs.

Explore different kinds of user-defined functions in Hive, including scalar UDFs that return a single output and user-defined aggregating functions for partial and full aggregations, with practical examples.

Learn to create and call Hive user-defined functions (UDFs) by packaging a Java class, defining its package and alias, and using the function in a Hive job.

Explore Hive user-defined aggregate functions (UDAFs), focusing on implementing partial aggregation, extending the base class, and handling input patterns to correctly evaluate and combine results.

Explore text file format as the default storage in Hive and sequence file format as a binary, space-efficient alternative. Understand when to use each for disk I/O and cross-tool sharing.

Discover the RCFile format for Hive, which stores relational tables in column groups to enable efficient column pruning. Learn how compression benefits low-cardinality columns and binary data storage.

Explore csv and json SerDe in Hive, detailing how serializer and deserializer interfaces convert unstructured bytes into table records, and parsing with custom delimiters and escape characters.

Explore hive avro serde and how it reads and writes avro data in hive tables, supporting backward compatibility, arbitrary nested schemas, nullable fields, and union types with transparent compression.

Explore Hive storage handlers, input and output formats, and how they enable reading from and writing to diverse storage systems within Hadoop, with seamless data integration.

Explore HBase, an open-source, distributed, Java-based storage system that runs on a distributed file system and is modeled after Bigtable to store billions of rows with sparse data.