English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 9h 13m | 3.04 GB
Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this Video Editions book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they’re built.
Inside:
- Introduction to big data systems
- Real-time processing of web-scale data
- Tools like Hadoop, Cassandra, and Storm
- Extensions to traditional database skills
This Video Editions book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful.
Table of Contents
0 A new paradigm for Big Data
1 Scaling with a traditional database
2 NoSQL is not a panacea
3 The problems with fully incremental architectures
4 Lambda Architecture
5 Batch and serving layers satisfy almost all properties
6 Recent trends in technology
7 Data model for Big Data
8 Data is raw
9 Data is immutable
10 The fact-based model for representing data
11 Graph schemas
12 Data model for Big Data Illustration
13 Tying everything together into data objects
14 Data storage on the batch layer
15 Storing a master dataset with a distributed filesystem
16 Data storage on the batch layer Illustration
17 Data storage in the batch layer with Pail
18 Storing the master dataset for SuperWebAnalytics.com
19 Batch layer
20 Recomputation algorithms vs. incremental algorithms
21 Scalability in the batch layer
22 Low-level nature of MapReduce
23 Pipe diagrams a higher-level way of thinking about batch computation
24 Batch layer Illustration
25 An introduction to JCascalog
26 Grouping and aggregators
27 Composition
28 An example batch layer Architecture and algorithms
29 Workflow overview
30 Deduplicate pageviews
31 An example batch layer Implementation
32 URL normalization
33 Serving layer
34 The serving layer solution to the normalization denormalization problem
35 Designing a serving layer for SuperWebAnalytics.com
36 Contrasting with a fully incremental solution
37 Comparing to the Lambda Architecture solution
38 Serving layer Illustration
39 Building the serving layer for SuperWebAnalytics.com
40 Realtime views
41 Storing realtime views
42 Challenges of incremental computation
43 Asynchronous versus synchronous updates
44 Realtime views Illustration
45 Queuing and stream processing
46 Stream processing
47 Higher-level, one-at-a-time stream processing
48 Guaranteeing message processing
49 SuperWebAnalytics.com speed layer
50 Topology structure
51 Queuing and stream processing Illustration
52 Implementing the SuperWebAnalytics.com uniques-over-time speed layer
53 Micro-batch stream processing
54 Micro-batch processing topologies
55 Core concepts of micro-batch stream processing
56 Extending pipe diagrams for micro-batch processing
57 Bounce-rate analysis
58 Another look at the bounce-rate-analysis example
59 Micro-batch stream processing Illustration
60 Finishing the SuperWebAnalytics.com speed layer
61 Fully fault-tolerant, in-memory, micro-batch processing
62 Lambda Architecture in depth
63 Batch and serving layers
64 Incremental batch processing – part 1
65 Incremental batch processing – part 2
66 Measuring and optimizing batch layer resource usage
67 Speed layer
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