Big Data: Hadoop| MapReduce| Hive| Pig| NoSQL| Mahout| Oozie

Gain practical skills to administer Hadoop environments, from single-node to multi-node clusters, covering architecture, MapReduce, replication, backup and recovery, maintenance, and high availability.

Compare traditional RDBMS with Hadoop’s distributed, scalable cluster that stores data as blocks on commodity hardware, enabling open source, schema-on-read, and avoiding data archiving for better analysis.

Explore how Hadoop couples a distributed storage layer with a MapReduce processing layer to handle large volumes of unstructured data, with open-source software and paid support.

Explore how the Hadoop distributed file system uses a master and slave daemon architecture with block-based storage and replication across a cluster.

Explore the Hadoop storage layer by learning how the distributed file system uses name nodes and data nodes to manage blocks, replication, and metadata for efficient data access.

Explore how Hadoop 1.0 core components manage blocks and replication across data nodes, with NameNode tracking metadata and JobTracker coordinating tasks with data locality and self-healing.

Understand how fs images and edit logs form Hadoop metadata, stored on disk and loaded into memory, and how the secondary name node merges them to reduce downtime.

Explore the role of the secondary name node in Hadoop's HDFS, including how it merges fsimage and edits, and why it is not a hot standby for the name node. Understand how block size, blocks, and replication affect metadata and cluster resilience.

Learn how hdfs coordinates write and read operations across blocks with NameNode and DataNodes, and how rack awareness guides block placement and replication.

Explore how HDFS uses a sequential block replication policy enabling reads during writes. Learn the Hadoop architecture—name node, secondary name node, data nodes—and MapReduce basics.

Practice essential Linux commands and file permission concepts, explore the Linux file system, manage users, networks, and troubleshooting logs, and prepare with hands-on assignments and reference resources.

Install a single-node Hadoop cluster and scale to multi-node setups, and learn the architecture including name node, data node, secondary name node, and MapReduce with replication.

Build a Hadoop lab by creating a Linux VM in VMware Workstation, configuring hardware, mounting a CentOS 6.3 ISO, and installing in command-line mode using expert.

Install and boot the Linux system, reboot, log in as root, disable SELinux, and set IP address and gateway using the virtual network editor.

Configure the host name and gateway on a Linux VM by editing /etc/hosts and network config, set a static IP and DNS, then restart the network and verify with ifconfig.

connect and mount the iso in a linux vm, copy the iso contents to staging directories, then create a local yum repository file for offline installation of oozie components.

Install ssh access by using yum to install openssh-clients from a local repository. Configure passwordless ssh by generating a public/private key pair and enabling key-based authentication.

Copy the public key to the authorized_keys file to enable passwordless ssh, configure ssh settings for non-password prompts, and set up a dedicated Hadoop user and environment for deployment.

Set up a single-node Hadoop cluster by configuring replication factor to 1, block size to 128 MB, and editing masters, slaves, and mapreduce properties, after installing Java.

Create an ssh keypair for the Hadoop user, enable passwordless ssh, and configure environment variables for Hadoop and Java to enable runtime recognition and startup of Hadoop daemons.

Start and verify a Hadoop mapreduce cluster by launching NameNode, JobTracker, TaskTracker, use hadoop fs -put and -ls, and learn VM snapshotting and cloning to multi node.

Clone and configure a Hadoop cluster using virtual machines, creating single and multi-node setups with name node, secondary name node, job tracker, and slave daemons, while allocating RAM.

Change the host name and IP address on each virtual machine, delete the MAC ID file to avoid conflicts, reboot, and verify new addresses with ifconfig to ensure unique IPs.

Log in and edit core-site.xml, mapred-site.xml, and masters and slaves to configure the namenode, secondary namenode, data nodes, and job tracker. Set replication factor to match the three data nodes.

Format the name node and reinitialize the Hadoop data file system, stopping mapred and clearing data blocks on cloned machines to avoid cluster data; formatting yields a new namespace ID.

Create a Hadoop cluster with a name node and data nodes, ensuring namespace id consistency across version files, then start DFS and MapReduce daemons.

Understand hdfs as a self-recovery, distributed file system that replicates data blocks across nodes, guided by a name node master, data nodes, and a job tracker with task trackers.

Learn to configure Hadoop core files, including name node, secondary name node, and job and task trackers, plus runtime settings for Java home, heap memory, replication factor, and block size.

Configure a Hadoop cluster by learning how environment variables and bashrc relate to heap size, and enable passwordless ssh for seamless data node communication via core config files.

Configure rack awareness to distribute data blocks evenly across racks and recover from rack failures, using shell scripts and cluster management commands.

Create a topology data file listing slave node ip addresses with rack numbers and use scripts to distribute data blobs across nodes. Enable rack-aware fault tolerance for the cluster.

Configure rack awareness to evenly distribute data across racks, enabling automatic recovery when nodes fail, and validate with fsck, replication factor, and rack topologies in physical data centers.

Learn to configure and administer a Hadoop cluster, including commissioning and decommissioning nodes, managing replication factor, and using the MapReduce framework through hands-on startup commands for a single-node setup.

Learn to manage Hadoop clusters with NameNode operations, use dfsadmin report to inspect data nodes and capacity, and apply safe mode and read-only maintenance during backups, avoiding NameNode format.

Learn to use hadoop fs commands to move files from local to hdfs, view with cat, and manage block size and replication; switch cluster between read-only and read-write modes.

Transfer a local file to HDFS, exit safe mode, view file contents, and manage cluster tasks like backups, upgrades, and balancing data blocks.

Learn to use the Hadoop fs command to list, create, copy, view, and get files in the Hadoop file system, noting its distributed and replicated data across the cluster.

Manage directories and files in the Hadoop file system, deleting with fs -rm -r and performing data injection by copying data from local using put and get to transfer files.

Learn to use Hadoop fs and fse commands to locate physical file blocks, print block reports, and assess replication and health across the cluster.

Learn how hdfs writes data with replication, updates the fsimage and edits, and uses block reports and fsck to verify data integrity across the name node and datanodes.

Explore how checkpointing maintains Hadoop’s name node reliability by merging in-memory edits with on-disk fsimage, updating the secondary name node, and backing up to local storage through copy commands.

Examine four methods to merge filesystem image and edits: secondary name node, checkpoint-driven merges, safe mode merging, and restarting the name node.

Explore how block scanner reports reveal block health and replication status across a Hadoop cluster, view real-time block states, and configure topology to enable data recovery when cluster components fail.

Learn to monitor the health and replication of data blocks with the block scanner report, view real-time state in a web browser, and configure topology for distributed clusters.

learn how to commission a data node in a multi-node hadoop cluster, start dfs daemons, update hosts and slaves files, and verify capacity and data blocks.

Set up and configure a Hadoop cluster by adding data nodes, updating hosts, refreshing configuration, and starting the namenode and data-node daemons to ensure reliable inter-node communication and data availability.

Balance data blocks across new and existing data nodes using the balancer with a 10 percent threshold, commission nodes, decommission as needed, and validate with dfsadmin -report.

Explore storage considerations in a Hadoop cluster, including commissioning and decommissioning nodes, balancing blocks, and defaults like 64 MB (Hadoop 1) and 128 MB (Hadoop 2).

Master secondary sort in Hadoop to order output by values within key groups, not by keys. Discover partitioning and grouping, including partitioners and natural grouping of map outputs to reducers.

Create a composite key class for MapReduce, implementing writable and comparable, using data input/output for serialization, and comparing by key then value to yield sorted output.

Explore building and using a composite key in a Hadoop mapreduce workflow, including tokenizing input, sorting by key, and applying a custom group comparator for reducer grouping.

Implement word count with a composite key and custom grouping in MapReduce, using a hash partitioner to group values by key and route data to reducers.

Explore the importance of partitioning and grouping in MapReduce by building a word-based composite key, implementing a reducer, and configuring the job with proper input, output, and error handling.

Initialize inputs, run a Hadoop MapReduce jar on cloud, reuse the previous word-count output, view HDFS with fs ls, and learn secondary sorting using composite keys, partitioner, and grouping.

Explore how to implement joins in Hadoop, including map-side and reduce-side joins, using examples from orders and customers, and learn when to use secondary sort and in memory map-side joins.

Create a configuration object and use Hadoop's distributed cache to share a small dataset across map tasks, enabling a map join with a lookup map from customer IDs to names.

Explore the setup method as a lifecycle initialization step that runs once, handles configuration changes, and prepares data by reading files, loading datasets, and building hash maps.

Learn how a map-side join works in MapReduce by building a customer data map, joining lines by customer id, and writing results with context.write.

Learn how to run a Hadoop MapReduce workflow: upload jars and datasets, inspect HDFS, and perform map-side and reduce-side joins on customer id to output joined customer orders.

Explore how a combiner in Hadoop reduces map output before the reduce stage, improving efficiency by shrinking intermediate data, with caveats about when not to apply.

Learn how to implement a word count MapReduce job in Hadoop, using a combiner to optimize tokenization, mapping, reducing, and producing word counts and logs.

Upload the combiner jar and an input file, run the Hadoop job, and observe how the combiner reduces map outputs before the final reduce.

Learn to perform a real-world mapreduce join between movies and ratings datasets to count total ratings per movie, using a Maven project and map and reduce steps.

Develop a ratings mapper that tokenizes lines, extracts movie names and ratings, and joins movie and rating data to produce a sorted list of movie ratings.

Design a map-based workflow to join movie data with ratings, extract movie names, and count how many ratings each movie has, outputting movie name and rating count.

Develop a movie and rating calc jar that parses comma-delimited input, splits lines, and joins movie and ratings data to count ratings per movie and per user.

Count the number of ratings per user by joining the users and ratings datasets with a mapreduce workflow, extracting user IDs and aggregating counts.

Develop a user rating reducer in a Hadoop MapReduce workflow, parsing lines into user and movie IDs and aggregating per-user ratings.

Demonstrate a MapReduce workflow that processes user IDs and movie IDs from ratings data to produce per-user movie counts, with a GUI view of the results.

Learn the basics of Yarn, the cluster resource manager in Hadoop 2.0, including the resource manager, node managers, containers, and application masters, and compare with Hadoop 1.

Explore how the node manager registers with the resource manager, allocates containers, reports health via heartbeats, and supports application masters in map-reduce and hive jobs in highly available YARN clusters.

Build and run a MapReduce program with Hadoop in Eclipse. Configure the Hadoop libraries and execute a first MapReduce job on a QuickStart VM, debugging setup issues.

Run a MapReduce program locally to debug a word count with a tokenizer mapper and main arguments, then export and run on a cluster to verify input and output.

Explore the hdfs file system through a java api demo, creating and organizing packages, building and running a local job, and handling file creation and existence checks.

Build and test a word count program in Java by creating a chapter 3 package and configuring input and output; run a local job with a debug script.

Explore word count with tools in a Hadoop MapReduce workflow, executing a load program with simple IO, building Java classes, packages, and reducers, and debugging a log processor.

Build a log processor by creating packages and classes, wiring a main method, and managing map and reduce jobs and dependencies in a Java-based workflow.

Delve into advanced mapreduce and pig by building custom key types and data formats, and running end-to-end jobs with input, output, and detailed logs for debugging.

Execute advanced MapReduce jobs, manage input files in the file system, and use job control to run jobs and observe outputs.

The lecture demonstrates executing a simple big data program, performing a filter, sort, and join on sample data to reveal country insights.

Learn to create tables in Hive, load text data, run selects, perform joins on country data, and export results to analyze life expectancy in the HDI dataset.

Create a new Java class, read and copy files, fix import errors, and implement a text-searching and indexing workflow while debugging and running configurations.

Execute and export a text out in inverted indexer workflow, build chapter 7 jobs, and extract an index from input data such as access logs using a graph extractor.

Explore how to set up a Hadoop MapReduce environment using QuickStart in a virtual box, create a Java project in Eclipse, and attach Hadoop libraries for a word count program.

Configure the Java build path for a Hadoop MapReduce project by adding jars, compiling a word count, and running it locally with input and output.

Run a local MapReduce word count job from a local file system to a VM, exporting, executing, and inspecting the resulting outputs and logs.

Set up a MapReduce workflow by creating a new package and a job class, then run the program to read input files and write outputs to a target folder. Trace the data flow by examining the read and write operations and how file contents are moved and transformed within the MapReduce setup.

Explores the sequence file format in Hadoop, showing how sequence writers compress and encode data and how sequence readers decode it into readable output.

Parse weblogs using a Hadoop MapReduce workflow to read and write files and interpret weblog contents into a target format.

Create a page view mapper in a mapreduce workflow, attach necessary libraries, set up Java programs, and run a job to generate page view counts from input data.

Learn to build and run a basic analytics program: create an engram job and a lines-matching words classifier, process input data to extract keywords, and explore advanced joins and graphs.

Learn how to perform map-side joins in Hadoop by implementing a web log mapper, configuring input and output, and running MapReduce programs to validate join results.

Learn to build and run a map reduce workflow for an inverted index, configure and compile mapreduce classes, and observe indexed data structures and configuration dumps in a Hadoop-like environment.

Build the core by scaffolding a package and creating classes like producer, comparator, and partitioner, then assemble and run the friend of a friend example to verify output on Windows.

Learn to run proximity-based word clustering and identify closest words, then build and run a page rank example, including creating a package and troubleshooting run-time issues.

In the cloud era local host output scenario, this lecture shows a program running on its own, reading input files, and displaying results that reveal page frequencies.

Build and run the final mapreduce program by constructing geotagging packages, defining places and playsets, compiling the code, and executing the programs to display logs.

Explore strands by implementing and running examples, observing select operations and console messages. Demonstrate building a Java API by creating a package and classes, copying and pasting code for testing.

Explore Java programming by creating new classes and using a factory. Learn file handling through select all, copy, and paste operations as you build and organize code and references.

Builds jobs, an object factory, and actions within packages, then copy and paste rules to assemble executors and tasks.

Configure library paths and add missing jars, then troubleshoot build issues in a NoSQL and Java API context. Run programs like ftb export manager to observe formatting and results.

Explore Apache Hive, an open source data warehouse on Hadoop for analyzing large data sets stored in HDFS using Hive's query language.

Explore Hive databases by creating, using, listing, and dropping databases with if not exists checks, then switch to your database to define employee tables and load tab-delimited data.

Load data into a table using load data local with local or dfs inputs, prevent duplicates with overwrite, and use alter table to rename, add, or drop columns.

Replace a column and add columns in Hive, create and drop tables, and explain external versus managed and temporary tables.

Understand when to use external tables in Hive to keep data at its location and avoid deletion on drop, versus internal and embedded tables that manage or delete data.

Discover how the hive metastore manages metadata with an embedded, local, or remote database across hive configurations on separate hosts, and how the hive server communicates via a network api.

Explore how hive stores data in tables, uses a default text format, and supports complex types such as maps and arrays, with object inspector and java code driving parsing.

Learn to create a partition table, with columns like employee ID, name, salary, and years of training, and load records into partitions using insert statements.

Use insert overwrite table to create and load a partition table, filter employees by year joining 2011 with a where clause, and select from employee to verify results.

Enable dynamic partitioning for a partition table by setting properties to non-strict mode, and understand how exceptions arise when dynamic partitioning is not enabled.

Explore Hive bucketing and partitioning, creating separate files by year and using hash-based buckets to speed queries on large datasets.

Explore partitioning and bucketing in Hive: create a table, assign four buckets by employee ID, and enable static and dynamic partitioning with proper properties.

Explore Hive joins using a practical customers and orders example, joining on customer id to fetch customer names and product names, while learning table creation and data loading.

Learn how hive joins work and optimize them with map join, bucket map join, sort-merge bucket join, and skew handling to improve performance on large data sets.

Explore how to handle skewed keys in Hive joins by processing skewed keys with in-memory hash tables and choosing map-side or reduce-side joins to optimize performance.

Discover how hive serde converts and parses data, using text and alternative formats, handling json, maps, lists, and structs with object inspector and column-name matching for hive tables.

Explore serde in hive by uploading a jar, adding a job, creating a table, and loading json data with java code and a fully qualified class name.

Learn how to create and deploy Hive user defined functions (UDF and UDAF) in Java, build a job project, and apply uppercase transformations to table data.

Learn how to implement a Hive UDF by creating a function, supplying a fully qualified class name, uploading the job, and applying the function to convert data to uppercase.

Explore how Hive UDFs compute the maximum value across files using the Mad Max function, with partial aggregation. See how Hive merges partial results to produce the final maximum.

Create and demonstrate a max function to compute the maximum customer id from the customer table, using an aggregate function and a user-defined function approach.

Explore a Hive use case modeling a movie ratings dataset with movies and users tables, loading data, and using joins and aggregations to identify top rated movies and active users.

Explore Hive concepts with a hands-on demonstration, and learn that Hive is not a database but a data warehouse layer using structured tables, metadata, and sql-like commands with storage formats.

Compare internal and external tables in Hive, noting that internal tables store data, while external tables reference dfs data; dropping metadata-only affects the table, not the data.

Load data into tables from the file system or dfs using insert into table select, then distribute it into multiple tables with conditions.

Explore date and mathematical functions in data workflows, including unix time, date parts, day differences, and next day or month calculations, plus string operations like upper, lower, padding, and trim.

Explore conditional statements and null checks in data queries, including if-else logic and a function for the first non-null value. Learn string functions like split and substring.

Explore how the explode function expands a column into separate rows and how lateral view enables querying exploded data, including maps and keys and values.

Explore how Hive sorting uses MapReduce to order data with order by and reducers, producing a single fully ordered output. Apply distributed by and sort by for non-overlapping value ranges.

Explore how Hive joins combine tables on join conditions, including left, right, and full outer joins, with multi-table examples and memory-friendly streaming of the largest table.

Explore map join techniques for small tables by loading data into memory, enabling map join properties, and using bucketed joins with bucketed input format to run as a mapper-only job.

Partitioning divides a table into subdirectories by partition column values, speeding queries by scanning only relevant data. Static partitions fix values like 2012 and 2013; dynamic partitions load data automatically.

Learn how Hive dynamic partitioning creates and loads data into partitions on the fly using partition by, and when to use static vs dynamic partitions with enabling properties.

Explore data definition language tasks using the alter command to rename tables, modify and add columns, replace columns, describe tables, and adjust table properties and file formats.

This lecture demonstrates using the msck command to manage hive partitions, explaining static and dynamic partitioning for external tables, including creating, dropping, and locating partitions in a directory structure.

Learn how bucketing uses a hashing function to split data into buckets within partitions, improving join performance, data sampling, and overall efficiency alongside partitioning.

Explore table sampling on a bucketed table by selecting samples from the first bucket or four buckets. Learn how limiting rows and focusing on columns yields practical samples for queries.

Archive infrequently used data to free memory and optimize performance, and learn how to enable archiving by default, configure settings, and run a command to move files to archive storage.

Learn how the rank and dense_rank functions assign positions within partitions using order by, handle ties, and extract top records per group with partitioning by columns.

Explore how indexes speed searches across tables and partitions, compare compact and bitmap indexes, and learn index maintenance like online builds and rebuilds.

Explore the advantages of views and altering views, and analyze when to use indexing—bitmap indexes and others—for faster queries on large datasets.

Demonstrate indexing in big data by showing how an index stores a selected data subset without containing the full table, enabling views, column selection, limits, and the concat function.

Learn how to safeguard confidential bank customer data by using views to restrict columns, join tables, and create a new table as shorthand for a complex query.

Execute hive commands from the bash shell to run single or multiple queries ending with semicolons. Use hive scripts with variable substitution to connect to a remote hive server.

Set hive variables in a session with set, use them in queries, and note their session scope; persist via an rc file for cross-session access.

Explore Hive variables and hiveconf in the Bash shell, declare and use multiple variables in single queries and scripts, pass values at runtime, and apply variables in Hive queries.

Configure Hive variables by parsing variable names and values. Run scripts in the Hive shell using the source command and execute Unix-style commands with exclamation marks.

Learn how to substitute values from an existing variable into a new one, replacing hardcoded values, using properties and variable references to populate a table and enable writing results.

Explore how to count word frequencies from a comma separated file by loading into a single column, applying split, then map and reduce to compute counts.

Hive architecture outlines a five-component framework—user interface, driver, compiler, metastore, and execution engine—that builds an execution plan from metadata and coordinates data storage, reading and writing, to deliver results.

Explore how to achieve Hive query parallelism by running independent stages in parallel, enabling a property to speed joins, and weighing benefits against risks like deadlocks.

Explore Hive table properties that guide data processing and data selection, including skip lines for headers and footers. Load data with skip header lines to get actual data.

Explore the null format property, which treats designated values as null in three-column data, and learn how to configure table properties to mark empty fields as null values.

Learn how to configure Hive table properties for various file formats, including default formats, compression codecs, bytes in each compression, index options, and bloom filters on selected columns.

Drop table in Hive removes metadata and data for internal tables, while external tables lose only the Hive linkage, leaving data in DFS. Purge settings control permanent deletion.

Learn how to capture changes in Hive tables using slowly changing dimensions, including type 1 overwriting and type 2 history preservation with effective dates and versioning.

Implement SCD by using a full outer join to merge updates and new records into the target, overwriting its contents, and applying change data capture logic.

Demonstrate an SCD example by using a full outer join across tables to distinguish new, updated, and unchanged records, and trigger inserts or updates accordingly.

Load xml data into Hive using a ready-made SerDe, download and add it to the Hive environment, and parse XML tags into table columns such as title, author, and country.

Learn to define a Hive table for XML data, specify input and output formats, map XML tags to columns like title, author, country, company, and price, and load XML.

Learn how to prevent accidental data loss in Hive by using no drop and enable offline options on tables and partitions, and toggle drop and offline states.

Learn how to create immutable tables by setting the immutable property to true, see how insert into behaves on first load versus subsequent inserts, and understand appending versus overwriting data.

Learn how to configure hive settings within a session and via the hive configuration file, and understand cartesian products as cross joins that pair every row from two tables.

Learn how Hive creates multiple tables from a single data file by linking metadata, and how column counts may differ from the data file.

Explore techniques to reduce small Hive files by merging them into fewer files using Hive settings, while understanding rlike and like patterns for string matching and trimming considerations.

Explore Hive configuration settings, including block size and DFS size implications on input splits, default file format as text, and how strict versus non-strict mode affects ordering and reducers.

Configure speculative execution in hive to run duplicate tasks on other nodes, speeding up slow jobs while balancing potential performance penalties, and enable map join and bucketing options.

Discover how compression reduces file sizes to save memory and network bandwidth in hive workflows, enabling map output compression with codecs like gzip and snappy.

Explore Hive in embedded, local, remote, and distributed modes, with different Hive server setups and Derby databases. Learn when to use each mode and how to switch modes via configuration.

Explore file compression in Hive, its benefits for storage and network transfer, and how input and map output data can be compressed, plus how to configure codecs like Snappy.

Explore Hive execution modes, including single JVM, local mode, standalone mode, distributed mode, and remote mode, and learn how to switch between them for faster local processing and production deployments.

Explain the difference between internal and external tables in Hive. Internal tables lose data on drop; external tables retain data at a fixed location.

Explore Apache Pig, a Hadoop ecosystem scripting platform that uses Pig Latin to analyze large datasets with simplified syntax, operator-based tasks, and automatic optimization for non-Java programmers.

Apache Pig offers a high-level language with user defined functions, handling structured to unstructured data, and simplifies joins and mapreduce tasks, with optional schema unlike Hive.

Compare pig latin scripting for batch analysis of large data sets with hive's queries, highlighting data types: structured, semi-structured, and unstructured, and map-based job execution.

Explore Apache Pig local and mapreduce modes, using the pig shell, batch mode, and embedded mode with pig latin scripts on local or distributed file systems.

Launch local mode to run processes on the local file system. Create and load a sample dataset, define field names and a separator, and verify contents with dump.

Explore Pig data types like int, long, float, double, chararray, boolean, date, big integer and decimal; learn bag, map, and tuple, plus core operators like load, dump.

Explore loading data into a Pig relation and storing the output using load and store commands, with schema options, dump viewing, and local or dfs storage configurations.

Demonstrate loading data, then verify execution steps using dump, describe, explain, and illustrate commands to view contents, schema, and execution plans.

Explain Pig group operators by grouping employee data and describing the resulting schema. Demonstrate applying group by age, describing the grouping, and viewing the grouped results.

Explore the co group operator for grouping multiple relations, compare it to the group operator for a single relation, and learn to group by age across datasets.

Explore how to perform joins in Pig, including self-joins and outer joins, by loading two example datasets (customers and artists) and applying join keys to combine related records.

Learn how to perform joins and cross products in Pig, including self-joins and left, right, and full outer joins, plus union and split operations for data.

Explore union and split operators in Pig while mastering join, cross operator, and if statements to manipulate relations and data in a Hadoop workflow.

Learn how to use union operators to merge relations and split operators to partition data by value thresholds, routing records into low and high value relations with if conditions.

Learn to use key pig operators such as distinct, for each, order by, and limit, and see how to filter data and select specific fields in big data workflows.

Learn Pig functions, including internal aggregates like average, max, min, and string operations such as concat, with hands-on examples on loading a relation from a sample employee dataset.

Explore Pig functions in Hadoop by grouping data, computing max age with foreach, and implementing user defined functions in Java using Eclipse and Maven.

Write a simple udf to uppercase input values, handle zero-size inputs, and catch errors. Then export and register the job, and run it in a Hadoop Pig workflow using Java.

Discover Pig and Pig Latin, a scripting language for exploring datasets. See how Pig Latin programs apply transformations to input data, creating a dataflow executed locally or distributed mode.

install the Hortonworks sandbox via VirtualBox, import the appliance, and start the VM to access the sandbox in a browser at 127.0.0.1:8888.

Master pig latin fundamentals by exploring simple and complex data types, including bag, tuple, and map, and applying basic arithmetic and relational operators.

Learn to download, extract, and upload two csv data files to hdfs on the Hortonworks data platform, then run a script to load, process, and inspect the data.

Execute a Pig script to read data, generate a player id, group by maximum runs, and join with the players data while monitoring logs and handling errors.

Learn Pig Latin basics by loading text data, tokenizing into words, and grouping to generate counts. See practical examples with file uploads and stock data, scripts, and saved results.

Learn to load and store data in Pig with load and store commands, define data types, and manage csv inputs through examples.

Upload data via a browser, create directories, and run a script to load and process csv-formatted data. Convert Excel to csv and store results using storage, verifying success in logs.

Load data into the dfs, define variables, flatten data with a for-each style process to extract date, high, low, and volume, then store results and check logs.

Explore debugging techniques in big data workflows using explain, describe, and illustrate to inspect data, schemas, and execution plans; learn loading and storing data with storage commands.

Explore Grunt Shell, an open-source command-line interface for Hadoop in the Hortonworks sandbox, covering local and cluster modes, basic commands, loading and storage, and debugging MapReduce scripts.

Learn to create and use UDFs in Apache Pig with Piggy Bank, writing in Java, Groovy, JavaScript, or Ruby, and apply them to data, including a Fahrenheit to Celsius example.

Trace the history of NoSQL from Google's big table to Amazon's distributed data stores, including key-value and column-family models, highlighting data explosion and tradeoffs between availability, consistency, performance, and search.

Explore schema agnostic databases in NoSQL systems, contrasting flexible storage without upfront schema with relational databases, and discuss how this impacts development, indexing, and querying.

Explore non-relational databases, comparing embedded data and the absence of fixed relationships with relational models, weighing normalization, data duplication, and fast queries across multiple machines.

Explore how enterprise NoSQL databases distribute data across commodity hardware to scale for massive data sets, improve high availability, and address transactions and enterprise considerations.

Explore how recent IT trends favor NoSQL databases over traditional RDBMS for unstructured data, addressing schema redesign, evolving data structures, and global distribution.

Explore how NoSQL manages unstructured data at scale, enabling entity extraction, enrichment, and flexible relationships, with schema-agnostic design and global replication.

Explore how to manage different data types across relational, column-family, and document stores. Learn how NoSQL and hybrid databases support variable schemas, fast queries, and appropriate consistency strategies.

Explore data stores in big data architectures: column stores with column families, key-value and document stores, and graph stores using subject-predicate-object triples to model relationships.

Explore hybrid NoSQL databases that blend document, key-value, and column storage to handle unstructured data and complex documents with fast indexing and versatile querying.

Apply the concept of consistency in databases by comparing strong and eventual consistency, and understand how replication, clustering, and schema choices influence data reliability.

Compare ACID and BASE approaches to transactions, explain tradeoffs among consistency, availability, and partitioning, and help decide when strong consistency matters for critical systems.

Develop applications on NoSQL by employing polyglot persistence, selecting appropriate data stores (key-value, document, column) for different data needs, and designing efficient cross-store queries and indexes.

Relational databases persist beside mainframes, while NoSQL addresses schema flexibility and performance for new data problems. Leverage search engines and text indexing to enable real-time analytics and dashboards.

Explore how Hadoop enables distributed batch processing with MapReduce and HDFS, using commodity storage to cut costs, and apply semantic technologies like RDF and SPARQL in NoSQL and cloud workflows.

Configure a key-value store to ensure availability with well-defined keys, indexing, and replication; balance eventual and transactional consistency using read strategies and partitioning.

versioning data in distributed key-value stores enables automatic cluster management, partitioning, and replication with conflict resolution through eventual consistency.

Explore Mahout, an Apache licensed Java library for machine learning with clustering, classification, and recommendation, featuring collaborative filtering, matrix factorization, and scalable algorithms.

Explore Mahout architecture, from Lucene Vectorizer, vectors, and matrices to Hadoop-based processing, covering evolutionary algorithms, frequency vector mining, and classification, regression, and dimensionality reduction, with practical AWS and installation notes.

Install mahout on a local ubuntu box by downloading the package, setting JAVA_HOME and MAHOUT_HOME, and configuring environment variables; explore movie lens data for a basic recommender engine.

Explore item-based recommendation by selecting a similarity measure, processing past user activity with Mahout on Hadoop, and generating unified recommendation outputs from input data.

Demonstrate a C-based Bayes classifier with a hands-on workflow: prepare data from the 20 newsgroups dataset, convert text to vectors, train and test the model, and export vectors.

Explore command line options for mahout workflows, detailing input sequences, clusters, vectors, distance measures, centroids, iterations, and output and dump options used in k-means clustering.

Explore canopy clustering as an efficient approach to grouping data, using thresholds and sampled initial clusters on input vectors to guide the clustering process.

Explore how a basic recommender uses collaborative filtering to suggest movies based on users' past ratings, using movielens data and both user-based and item-based approaches.

Explore practical big data processing with map-reduce basics, including setting up input data, creating sequence files, and running a word-count example to understand mapping and reducing.

Run the Mahout seqdumper command to generate sequence files from input, dumping into a sequence file and illustrating the key-value pair format.

Learn to run Hadoop code in eclipse by creating a new class, adding dependencies, generating sequence files from data, and executing the job to produce key-value output.

Create and read sequence files with a Java class, importing shared code to update sequence data and verify outputs in the Hadoop workflow.

Explore Apache Mahout, a Java library for machine learning techniques like classification, recommendation, and clustering. See how it supports big data with e-commerce recommendations and document categorization.

Explore real-world use cases of big data systems, from recommendation engines to spam filtering, text processing, and machine learning techniques like collaborative filtering, matrix factorization, classification, and clustering.

Explore how collaborative filtering and matrix factorization power recommendation systems for shopping sites and social networks by leveraging historical ratings to generate personalized item suggestions.

Explore how different similarity definitions, including Tanimoto distance and Euclidean distance, affect distance-based calculations and nearest-neighbor evaluations in big data analytics.

Explore Mahout with a hands-on exercise: install Mahout 0.9, set up in a Java IDE, and build a recommendation model using user preferences, references, and similarity calculations.

Explore nearest neighborhood models to generate recommendations using neighborhood and similarity measures. Experiment with parameters like number of neighbors and similarity to assess recommendations on the movie lens dataset.

Explore building and evaluating recommender systems using Mahout: create evaluators, build item-based and MF-based recommenders with neighborhood similarity, set 70 percent training, and compute statistics.

Explore canopy clustering in big data, learning how density-based groups form clusters and how to prepare vector data, thresholds, and Mahout-based implementations via the command line.

Explore how classification assigns a new observation to existing categories using a dataset of many variables and a news by date test case, with training and testing.

Learn how to transform and tokenize text into vector files, set up training and testing data, train a model, and evaluate performance using confusion metrics and file system outputs.

Develop and run a Naïve Bayes classifier from code by building a Java project, wiring dependencies, preparing input data, training and testing the model, and viewing results.

Explore k-means clustering, its initialization, and how cluster counts affect performance, while preparing input data and constructing sequences for effective partitioning.

Use logistic regression on stock market data to generate buy or sell signals from a structured dataset of date, open, high, low, close, and volume.