This crash course provides a comprehensive overview of Big Data and its key technologies. It covers the main tech tools in four main sections:

  1. Data Storage and Management
  2. Data Processing and Analytics
  3. Data Integration and ETL
  4. Data Visualization and Business Intelligence.

In each section, we explore popular technologies and tools used in the Big Data ecosystem. From storage systems like Hadoop and Cassandra to processing frameworks like Spark and Flink, and from integration tools like NiFi and StreamSets to visualization platforms like Tableau and Power BI, this course equips you with the foundational knowledge to work with Big Data. By exploring these technologies and concepts, you'll gain a solid understanding of how to store, process, integrate, and visualize large volumes of data, enabling you to harness the power of Big Data for insights and decision-making.

Section 1: Data Storage and Management

Big Data involves handling large volumes of data, and effective storage and management are crucial. Here are some key technologies in this area:

  1. Apache Hadoop: Hadoop is a popular open-source framework that enables distributed processing of large data sets across clusters. It consists of Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.
  2. Apache HBase: HBase is a scalable NoSQL database built on Hadoop. It provides random read/write access to large structured data, making it suitable for real-time applications and low-latency scenarios.
  3. Apache Cassandra: Cassandra is a highly scalable and distributed NoSQL database designed for handling massive amounts of data across multiple nodes. It offers high availability, fault tolerance, and low-latency access.
  4. Apache Kafka: Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. It provides high throughput, fault tolerance, and messaging capabilities.
  5. Apache Parquet and Apache ORC: Parquet and ORC are columnar storage file formats optimized for analytics workloads. They offer high compression and efficient query performance, enhancing data processing efficiency.
  6. Amazon S3: Amazon Simple Storage Service (S3) is an object storage service provided by AWS. It offers scalable, durable, and highly available storage for big data applications.
  7. Google Cloud Storage: Google Cloud Storage is a scalable and secure object storage service provided by GCP. It provides a reliable and cost-effective solution for storing and retrieving large datasets.
  8. Azure Data Lake Storage: Azure Data Lake Storage is a scalable and secure data lake solution offered by Microsoft Azure. It enables storage and analysis of big data with high throughput and low-latency access.
  9. Apache Druid: Druid is a high-performance, real-time analytics database designed for sub-second OLAP queries on large-scale datasets. It provides fast aggregations and interactive data exploration.
  10. Snowflake: Snowflake is a cloud-based data warehousing platform that provides scalability, performance, and concurrency for handling large datasets. It offers features like automatic scaling, instant cloning, and multi-cluster warehouses.

Section 2: Data Processing and Analytics