Big Data for Beginners 2026|Spark, Hadoop, kafka and more

Explore the evolution of Hadoop from DFS and Google File System to MapReduce, and learn how distributed storage with HDFS and distributed processing enable scalable big data analytics.

Explore how MapReduce counts word occurrences through map, shuffle, and reduce phases across four nodes in a distributed HDFS, using key-value pairs to form the final output.

Explore big data as large volumes with velocity and variety, evidenced by social media activity and Walmart transactions, and learn tools like Spark, Kafka, Hadoop, and Hive.

Explore the Hadoop 1.0 architecture, including name node and data node roles, replication factor, heartbeat checks, and secondary name node checkpoints, with insights into job tracker and task tracker components.

Explore how Hadoop 2.0 achieves highly available name nodes, with zookeeper and journal nodes, enables federation for scalable name node architecture, and introduces yarn with containers and an application master.

Hadoop 3.0 introduces Java eight support, performance optimizations, and a dual name node architecture with active and standby nodes, plus erasure coding using parity blocks to recover data without replication.

Learn how to download and install essential tools, set up a Cloudera virtual environment, allocate resources, and connect via SSH to practice Hadoop and Hive.

Explore practical Hadoop commands for managing HDFS, including listing, copying data from local to HDFS, moving and copying within HDFS, and reading files with text.

Explore serialization formats in Hadoop and compare row and column storage to understand block-based data organization, query implications, and analytical versus transactional workloads.

Explore serialization basics and compare sequence, rcfile, avro, and parquet formats, highlighting transmission speed, compression, and schema evolution for big data applications.

Explore core Hadoop concepts and interview-ready topics: hdfs basics, MapReduce, replication factor, zookeeper, split brain, serialization formats (avro, parquet, orc), columnar storage, small files, rack awareness, and MapReduce optimization.

Sqoop introduction: connect relational databases to HDFS using JDBC, import and export data between MySQL and HDFS, leverage parallelism with mappers, and perform incremental loads.

Learn how to perform a Sqoop import from MySQL to HDFS using JDBC, importing the customers table into a Hadoop directory, and handling append and overwrite options.

Explore how Sqoop uses multiple mappers to parallelize importing from a customer table by splitting on the primary key, typically customer_id, with default four mappers.

Import only a portion of data from a table using a where clause (waiver clause) or a free-form query, and optionally select specific columns.

Master Sqoop imports by changing delimiters with fields terminated by and lines terminated by, swapping to pipe from comma, then verify data using scope eval against MySQL.

Learn how incremental import works with scope, importing only new records based on a last value and a check column, stored in a meta store via a scope job.

Store passwords in a file and reference them via scope to avoid exposure, then restrict file permissions. Encrypt passwords with JCS and use an alias in scope imports for retrieval.

Learn how scoop performs incremental last-modified imports to keep HDFS and MySQL in sync by comparing on order id and order date, updating changed records.

Import data into Scope in multiple serialized formats, including sequence, Avro, Parquet, and RC. Create a Hive table to store RC data and query it via Hive.

Import all tables at once with import all tables, organize data under a warehouse directory, and exclude tables to import only selected ones across the six retail_db tables.

Learn to handle null data during import with scope import by replacing string column nulls with a placeholder and non-string nulls with zero, enabling Hive queries on HDFS text files.

Learn how to export data from HDFS to MySQL using Sqoop export, including staging tables, the clear staging option, and data validation.

Explore Sqoop performance tuning for imports and exports by tuning mappers, using direct native drivers when available, and adjusting fetch size and export parameters to balance memory and throughput.

Mastering big data course offers Sqoop interview prep focused on import commands, mappers, filtering, incremental loads, job automation, and data serialization formats such as Avro, Parquet, and ORC.

Export data from MySQL and prepare Hive-ready datasets on the edge node, then copy the files into HDFS and structure per table directories for Hive ingestion.

Learn how Hive functions as a data warehouse for structured and semi-structured data on HDFS, using the Hive query language similar to SQL to support analytical ETL workflows.

Create and load Hive tables using the Hive shell, define databases, load data from local and HDFS, and understand managed versus external tables.

Explore Hive table types: managed tables delete backend files on drop, while external tables preserve data after drop, ideal for staging versus target systems.

Explore Hive partitions, including static and dynamic approaches, and learn how partitioning reduces scans. Implement static load, static insert, and dynamic partitioning with country and language data.

Import relational data into hdfs using scoop, then create an external Hive table and query it, including a group by on state.

Learn how Hive bucketing breaks data into buckets to enable efficient queries and avoid full table scans, using modulus to assign ids to buckets.

Learn how hive handles schema evolution with Avro data and ABC schema files, using Sqoop incremental imports to adapt to added or dropped columns without breaking queries.

Execute hive queries from the edge node command prompt or script files with hive -f, creating databases and tables, loading data, and verifying results.

Explore Hive date functions, including unique timestamp, from UNIX time, date formats, extracting year, month, day, and computing date differences plus add/subtract days.

Explore Hive joins, including inner, outer, left, and right joins, and optimize with map join, bucket map join, sort-merge bucket join, and skew join techniques.

Learn how to create a partitioned external table on existing data and use MSCK repair table to add partitions to the Hive metastore, ensuring queries return the correct data.

Improve Hive performance by tuning partitions for uniform data, using bucketing and range bucketing, and leveraging map joins, skew joins, vectorization, and high parallel execution to speed queries.

Compare Hive and SQL by emphasizing schema on read versus schema on write, with Hive supporting semi-structured data like JSON for analytical data warehousing.

Master Hive basics, including select, filter, in, like, case queries and built-in numeric, string, and date functions, plus guidance on creating databases, tables, and loading data.

Explore key hive concepts for interviews, including hive vs sql, metastore and derby, managed vs external tables, loading data, partitioning, bucketing, and performance optimizations like vectorization and parallel execution.

Install Spark and Scala, set up Java 1.8, and configure JAVA_HOME and PATH to run locally. Then test code in Eclipse and deploy to a Hadoop cluster.

Scala’s compatibility with Java, its reduced boilerplate, and its statically typed, object-oriented design, as we prepare hands-on sessions on big data with Spark.

Set up Eclipse with the Scala sdk, create a Maven project, and write a hello world Scala program with a main method to run and observe output.

Learn basic Scala concepts by creating objects, packages, and a main program; explore val vs var, strings, type conversion, and common string methods through hands-on examples.

Master conditional statements in Scala, including if, if else, else if, and nested if, with hands-on examples using variables, printing, and logical operators.

Explore loops in Scala, mastering while and do-while behavior, for loops with ranges and lists, and using break and breakable to exit loops safely.

Explore defining and calling functions in Scala, including main entry points, parameters, returns, default values, and custom length functions that mimic built-in string operations.

Explore defining a Scala class and creating object instances, accessing members via dot notation, and using private versus public access to control method visibility.

Explore how a subclass inherits from a base class in Scala to enable code reuse, and learn about single, multilevel, hierarchical, multiple, and hybrid inheritance, with traits.

Master single inheritance by seeing how a grandfather class passes methods and properties to a father class. Learn how overriding resolves conflicts and reuses code.

Explore multilevel inheritance in Scala, where a child class inherits from a father, and the father inherits from a grandfather, collecting properties and methods across multiple levels.

Explore hierarchical inheritance in Scala, where multiple subclasses inherit from the same parent to reuse code, alongside single and multilevel inheritance.

Discover how Scala traits enable multiple inheritance by mixing traits into a class. A student class extends both school and college traits to reuse their behavior.

Explore hybrid inheritance in Scala by linking traits B and C to a base class and extending them into class D, with practical code examples.

Explore method overriding and method overloading in Scala, using inheritance and the override keyword to customize behavior across classes and signatures.

Explore singleton objects in Scala by using the object keyword to access members without instantiating a class, and learn how companion objects share access to private members with their classes.

Explore how a case class differs from a regular class, enabling immutable schemas, pattern matching, and instantiation without new, and its use for Spark schema design.

Explore abstraction and the final keyword in Scala by implementing abstract classes, hiding complex details, and demonstrating with examples like a TV remote.

Explore higher order functions and lambda expressions in Scala, learning how to pass and return functions, and use anonymous one-line lambdas for concise code.

Explore partially applied functions by turning a three-argument sum into a two-argument form with a constant parameter, illustrated with a login log example that uses the system date.

Explore currying in scala by transforming a three-argument sum into a curried form with brackets, demonstrating it yields the same result and improves readability.

Explore the option type in Scala as a container for optional values. Learn how some and none indicate presence, and how get or else supplies defaults.

Explore Scala pattern matching with match and case statements, including default cases, and learn practical examples comparing city patterns and numeric conditions.

Master exception handling in Scala using try catch finally to handle divide by zero with clear messages. Explore throwing custom exceptions and the throws annotation; finally executes.

Explore Scala collections, both mutable and immutable, including sets, sequences, lists, maps, vectors, queues, tuples, and arrays. Learn creation, iteration, and common operations like head, tail, size, and contains.

Master collection methods in Scala with map, flatMap, filter, count, exists, partition, reduceLeft/right, foldLeft/right, and scanLeft/right, plus practical examples on lists and numbers.

Explore how group by partitions a scala collection into a map of sub-collections and how grouped clusters elements into fixed-size sub-lists, with seniors and juniors as examples.

Explore variable arguments in Scala, enabling functions to accept any number of inputs using the star notation, and learn how to pass lists or arrays as varargs.

Learn to read text files in Scala, convert to string, and print lines. Explore list conversions, slicing lines, and writing or appending via Java I/O.

Learn Apache Spark, a fast open-source framework for large-scale data analytics that enables batch and real-time processing on clusters, built on Scala and supporting Python, Java, and SQL.

Explore why Spark is faster than MapReduce through a word count example, highlighting lazy evaluation, in-memory processing, and rdd transformations and actions.

Master rdd basics by reading and writing a CSV file with spark and scala, then filter by category and subcategory, and save results to text files with partition control.

Develop and deploy spark jobs from local development to a cluster by building a jar and using spark submit; load data from edge nodes or hdfs and run on cluster.

Analyze log data by reading a sample log file, filtering for warning and error, and uniting results, then count and sample records, and discuss when to use collect in memory.

Explore common RDD transformations and actions in Spark using map, flat map, filter, distinct, union, intersection, and subtract, then apply collect, count, take, and reduce on sample data.

Explore how a distributed key-value pair forms a pair RDD and apply transformations like group by key, reduce by key, map values to aggregate, transform, and extract keys and values.

Explore the word count use case by counting word frequencies with Spark versus MapReduce, using an input file, flatMap, map, and reduceByKey to group and sum keys.

Define a schema with a case class, read a text file into an RDD, map comma-split fields to state, capital, language, and country, and apply column-level filters on language.

Learn how to move from schema-based Spark RDD processing to using Row RDDs, including converting to Row, accessing by index, and when to use data frames.

Explore Spark data frames as a distributed collection of data organized into named columns, similar to an sql table, and learn how to create and query them.

Create data frames from RDD or schema, and apply transformations in Spark. Explore using Spark session, select columns, filter languages, and query via DSL or SQL.

Master spark dataframes by seamlessly reading and writing data using spark session and various formats (csv, parquet, orc, json) with options, headers, and delimiters, and inspecting schemas.

Learn how to read and write Avro data with Spark by adding a third-party Avro package, configuring com.databricks.spark.avro, and handling Avro files alongside Parquet and CSV formats.

Read and write XML data using spark xml, load books.xml, define the root tag and row tag as book, and manage jars to run the spark workflow.

Learn to read and print json data in Spark, from simple to nested multi-line json, using the multi-line option, print schema and data, and prepare for flattening complex structures.

Learn Spark write modes for json data, including default error, and overwrite, append, or ignore to manage existing files when saving a data frame.

Read multiple files without header by applying an external schema via a struct type, then partition the output by country code and language.

Read a csv into a data frame, persist it, and create a temporary view; use dsls and sql to filter by age over 45 and life sciences in job roles.