Fault Tolerance and Recovery: Data Safety in Distributed Computing

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Spark Storage Layer

Explores the Spark ecosystem, Resilient Distributed Datasets, and the storage layer abstraction in Spark.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Hadoop: Execution Models

Explores Hadoop's execution models, fault tolerance, data locality, and scheduling, highlighting the limitations of MapReduce and alternative distributed processing frameworks.

Big Data Challenges: Distributed Computing with Spark

Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Big Data: Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

Scheduling Decisions: Data Locality and Multitenancy

Explores data locality in scheduling decisions for multi-tenant platforms and discusses Hadoop's architecture, execution engine optimizations, and fault tolerance strategies.

Scaling up: Spark and Big Data

Explores the challenges of big data processing and introduces Spark as a solution.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Integrating Scalable Data Storage and Map Reduce Processing with Hadoop

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.