--- title: Big Data Tools description: This topic explores the software frameworks designed to process, store, and analyze massive datasets. Learners will understand the architecture and use cases of tools like Hadoop, Spark, and NoSQL databases for handling high-volume data. category: programming-tech subcategory: technical-skills difficulty: beginner, intermediate, advanced url: /subject/big-data-tools --- # Big Data Tools This topic explores the software frameworks designed to process, store, and analyze massive datasets. Learners will understand the architecture and use cases of tools like Hadoop, Spark, and NoSQL databases for handling high-volume data. ## Available Resources 2 Books • 3 Courses • 4 Websites ## Courses ### 1. Apache Spark with Scala Learn Apache Spark with Scala! Master big data processing with hands-on examples from expert Frank Kane. **Difficulty:** Intermediate | **Price:** Paid **Link:** https://www.udemy.com/course/apache-spark-with-scala-hands-on-with-big-data/ **Tags:** apache-spark, scala, big-data, distributed-computing, data-processing ### 2. Introduction to Big Data Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/big-data-introduction **Tags:** hadoop, hdfs, mapreduce, big-data-fundamentals ### 3. Big Data An eleven-module Coursera course from O.P. Jindal Global University covering Hadoop, HDFS, MapReduce, Hive, Pig, HBase and Apache Spark. Graded assignments use PySpark, Spark SQL and MLlib on real datasets, so you finish able to build distributed processing pipelines. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://www.coursera.org/learn/big-data-analytics-1 **Tags:** courses, technology-computer-science, data-science--ai ## Websites ### 1. Apache Spark Documentation Apache Spark's official documentation: programming guides for RDDs, DataFrames and Spark SQL, Structured Streaming and MLlib, plus cluster deployment, configuration and tuning references. Readers can write, submit and tune distributed data-processing jobs in Python, Scala, Java or R. **Difficulty:** Beginner | **Price:** Free **Link:** https://spark.apache.org/docs/latest/ **Tags:** apache-spark, spark-sql, structured-streaming, distributed-computing ### 2. hadoop.apache.org Official Apache Hadoop project page for the open-source framework enabling distributed storage (HDFS) and processing (MapReduce/YARN) of big data. It offers documentation, downloads, getting-started guides, and ecosystem resources to help you deploy and use Hadoop. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://hadoop.apache.org **Tags:** websites, technology-computer-science, data-science--ai ### 3. spark.apache.org Apache Spark's official site is the home of the open-source unified analytics engine for large-scale data processing. It offers downloads, detailed documentation, tutorials, API references for Spark (SQL, PySpark, R, Scala), and guidance to get started or contribute. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://spark.apache.org **Tags:** websites, technology-computer-science, data-science--ai ### 4. kafka.apache.org Apache Kafka's official site for the distributed event streaming platform. It provides project overview, downloads, and extensive documentation plus quickstarts and tutorials for using Kafka, its Streams and Connect ecosystem to build real-time data pipelines. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://kafka.apache.org **Tags:** websites, technology-computer-science, technical-skills ## Podcasts ### 1. Data Engineering Podcast **Author:** Tobias Macey Long-running interview podcast hosted by Tobias Macey in which practitioners and open-source maintainers discuss data pipelines, orchestration, streaming, data warehouses and lakehouses, data quality and platform architecture. Listeners gain a working view of current data engineering tools and design trade-offs. **Difficulty:** Intermediate | **Language:** en | **Price:** Free **Link:** https://www.dataengineeringpodcast.com **Tags:** data-engineering, data-pipelines, orchestration, data-infrastructure, streaming ## Books ### 1. Spark: The Definitive Guide **Author:** Bill Chambers, Matei Zaharia O'Reilly reference to Apache Spark 2.x, written by Spark's original creator and a Databricks engineer. Covers the structured APIs, DataFrames, Datasets, SQL, streaming and MLlib, leaving readers able to write and tune distributed jobs in Scala or Python. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1491912219?tag=edmonddante07-20 **Tags:** books, technology-computer-science, technical-skills ### 2. Designing Data-Intensive **Author:** Martin Kleppmann Martin Kleppmann's O'Reilly survey of the systems behind modern databases: storage engines, replication, partitioning, transactions, consensus and stream processing. Readers finish able to reason about the tradeoffs when choosing or combining data stores at scale. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1449373321?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/big-data-tools