Start Learning Big Data
Explore these Big Data tutorials and master the different technologies of Big Data. Choose where to begin, learn at your own pace:
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- What is Big Data?
- Big Data History, Technologies, Use cases
- Benefits of Big Data
- Reasons to Learn Big Data
- Top Big Data Tools
- Why Big Data
- Big Data Career Path
- Apache Flink- Big Data Processing Framework
- Big Data Use Cases- Hadoop, Spark, Flink Case Studies
- Big Data at Flipkart
- Big Data In Income tax Department
- Big Data In Banking Sector
- Big Data In Media & Entertainment
- Big Data In Automobile Industry
- Why Big Data is Gaining Hype
- Why Switch Career in Big Data
- Big Data In Union Budget
- Careers and Job Roles in Big Data
- Big Data Developer Skills
- Why Choose a Career in Big Data
- Switching Career from Mainframe to Big Data
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- Big Data Application- Income Tax Department
- How Big Data helps with Wildlife Conservation
- How Big Data helps in Agriculture
- How Big Data helps in Education Sector
- How Big Data helps in Travel Industry
- How Big Data helps in Telecom Industry
- Big Data in Healthcare- Real World Use-cases
- How Big Data helps in Retail Industry
- Real Time Big Data Applications
- BI Tools for Big Data Visualization
- Top 10 Latest Big Data Trends
- Top 50 Latest Big Data Quotes
- Hadoop High Availability
- Hadoop Schedulers
- Hadoop Distributed Cache
- Hadoop Automatic Failover
- Hadoop Limitations & Solutions
- Hadoop HBase Compaction & Data Locality in Hadoop
- Install Hadoop 2.6 Multinode Cluster
- Install Hadoop 3.x on Ubuntu
- Install Hadoop 2 on Ubuntu 16.0.4
- Spark Hadoop Cloudera Certifications
- Kafka + Hadoop Integration
- R + Hadoop Integration
- Big Data Use Cases- Hadoop, Spark, Flink Case Studies
- Introduction to Spark
- What is Spark?
- Spark Environment Setup for Ubuntu
- Spark Installation in Standalone Mode
- Spark Terminologies & Concepts
- Spark Ecosystem Components
- How does Spark Work?- Runtime Architecture
- Why Should I Learn Spark?
- Spark Features
- Spark Shell Commands
- Installing Multinode Clusters for Spark
- SparkContext and its Functions
- Spark RDD, Features, and Operations
- Ways to Create a Spark RDD
- Spark RDD Persistence & Caching
- Spark RDD Features
- Spark RDD Limitations
- Spark Transformations and Actions on RDDs
- Spark RDD Lineage
- Apache Spark Paired RDD
- Create Spark Projects in Eclipse
- Spark Notes for Beginners & Experienced
- Apache Spark Use Cases in Real Time
- Spark Career Opportunities
- Best Spark and Scala Books
- Spark Map vs FlatMap
- In-Memory Computation in Spark
- Lazy Evaluation in Spark
- Fault Tolerance in Spark
- Spark Directed Acyclic Graphs (DAG)
- Spark Cluster Managers
- Spark – Hadoop Compatibility
- Performance Tuning in Spark
- Apache Spark Executor
- Apache Spark Stages
- Limitations & Drawbacks of Spark
- Introduction to Spark
- Data Type Mapping between R and Spark
- Batch Processing vs Real-Time Processing
- Apache Mesos- Architecture & Working
- Best Apache Mesos Books
- Big Data Use Cases- Hadoop, Spark, Flink Case Studies
- Apache Flink vs Apache Spark
- Hadoop vs Spark vs Flink
- Spark vs Hadoop MapReduce
- Apache Spark Certifications
- Cloudera Certifications
- Spark Quiz- Part 1
- Spark Quiz- Part 2
- Spark Quiz- Part 3
- Spark Quiz- Part 4
- Spark Quiz- Part 5
- Spark Quiz- Part 6
- Introduction to Spark SQL
- Apache Spark SQL Tutorial
- Spark SQL Features
- DataFrames in Spark SQL
- Spark SQL DataSets
- Optimization in Spark SQL
- Spark SQL Performance Tuning
- RDD vs DataFrame vs DataSet
- SparkR DataFrame and Operations
- Introduction to Spark Streaming
- Spark Streaming- DStreams (Discretized Streams)
- Transformations in Spark Streaming
- Checkpointing in Spark Streaming
- Structured Streaming in SparkR
- Creating SparkDataFrames in SparkR
- GraphX API in Spark
- Spark GraphX Features
- Graph Algorithms GraphX
- Spark Machine Learning with R
- Apache Spark MLlib Algorithm Featurization
- Spark MLlib Data Types
- Spark Machine Learning Algorithm
- Spark Streaming vs Apache Storm
- Spark SQL vs Hive
- Spark Interview Questions- Part 1
- Spark Interview Questions- Part 2
- Spark Interview Questions- Part 3
- Spark Interview Questions- Part 4
Exploring the Big Data world
Let’s take a look at some facts about Big Data and its philosophies.
What is Big Data?
Big Data is the data which cannot be managed by using traditional databases. Here is Gartner’s definition: The Data sets with huge volume, generated in different varieties with high velocity is termed as Big Data. These are considered as 3 Vs of Big Data.
These humongous volumes of data can be used to generate advanced patterns & address business problems you wouldn’t have been able to handle earlier.
History of Big Data
Roger Magoulas, in 2005, coined the term ‘Big Data’. In the same year, the development of Hadoop started. It is an open-source framework that could process both structured and unstructured data. This was built on top of Google’s MapReduce and crafted by Yahoo!. Today, the term Big Data pertains to the study and applications of data sets too complex for traditional data processing software to handle. This concept faces challenges in capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source.
Roger Magoulas