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

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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 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.

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.

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.

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.