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Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s importan
Why Is Big Data Important?
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
- Determining root causes of failures, issues and defects in near-real time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behavior before it affects your organization.
t. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Course Curriculum
Section 1: Introduction | |||
1.1. Introduction To The Course | FREE | 00:30:00 | |
1.3. Big Data Challenges | 00:00:20 | ||
1.4. Big Data Characteristics | 00:30:00 | ||
1.5. Problems In Capitalizing On Big Data | 01:00:00 | ||
1.6. Solving Big Data Problems | 00:25:00 | ||
1.7. Challenges Of Relational Databases | 00:40:00 | ||
Section 2: MapReduce And Hadoop | |||
2.1. MapReduce And Hadoop | 00:00:00 | ||
2.2. MapReduce Algorithm | 00:00:00 | ||
2.3. Introducing Hadoop | 00:00:00 | ||
Section 3: Hadoop Distributed File System | |||
3.1.Hadoop Distributed File System | 00:00:00 | ||
Section 4: Hadoop Infrastructure | |||
4.1. Hadoop Infrastructure | 00:00:00 | ||
4.2. YARN | 00:00:00 | ||
Section 5: Programming Hadoop | |||
5.1. Programming Hadoop | 00:00:00 | ||
Section 6: Hive | |||
6.1. Hive | 00:00:00 | ||
6.2. Hive Architecture | 00:00:00 | ||
6.3. Hive Data Model | 00:00:00 | ||
6.4. Hive Queries | 00:00:00 | ||
6.5. When To Use Hive | 00:00:00 | ||
Section 7: Pig | |||
7.2. Pig Data Model | 00:00:00 | ||
7.3. Pig Latin | 00:00:00 | ||
7.4. Pig Example | 00:00:00 | ||
7.5. When To Use Pig | 00:00:00 | ||
Section 8: Scalding | |||
8.1. Scalding | 00:00:00 | ||
8.2. Programming With Scalding | 00:00:00 | ||
8.3. When To Use Scalding. | 00:00:00 | ||
Section 9: Hadoop Ecosystem | |||
9.1. Hadoop Ecosystem | 00:00:00 | ||
9.2. HBase.mp4 | 00:00:00 | ||
9.3. When To Use HBase | 00:00:00 | ||
9.4. Beyond Classic Hadoop Spark And Flink | 00:00:00 | ||
Section 10: NoSQL | |||
10.1. SQL Stores | 00:00:00 | ||
10.2. KeyValue Stores | 00:00:00 | ||
10.3. Columnar Stores | 00:00:00 | ||
10.4. Document Stores | 00:00:00 | ||
10.5. Graph Stores | 00:00:00 | ||
10.6. Data Modeling For NoSQL Stores | 00:00:00 | ||
Section 11: Streaming | |||
11.1.Streaming | 00:00:00 | ||
11.2. Storm | 00:00:00 | ||
11.3.Spark And Flink Streaming | 00:00:00 | ||
11.4. Lambda Architecture | 00:00:00 | ||
Section 12: Big Data And NoSQL In The Enterprise | |||
12.1. Introducing Big Data And NoSQL In The Enterprise | 00:00:00 | ||
12.2. Polyglot Persistence | 00:00:00 | ||
12.3. Seven Habits Of Successful Big Data And NoSQL Projects | 00:00:00 | ||
Section 13: Wrap Up | |||
13.1. WrapUp | 00:00:00 |
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