Jan-Willem Middelburg
The Enterprise Big Data Framework
Building Critical Capabilities to Win in the Data Economy
Jan-Willem Middelburg
The Enterprise Big Data Framework
Building Critical Capabilities to Win in the Data Economy
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Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.
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Transform enterprise big data into valuable assets with this comprehensive guide to data analysis, data engineering, algorithm design and data architecture.
Produktdetails
- Produktdetails
- Verlag: Kogan Page Ltd
- Erscheinungstermin: 3. November 2023
- Englisch
- Abmessung: 238mm x 168mm x 29mm
- Gewicht: 830g
- ISBN-13: 9781398601710
- ISBN-10: 1398601713
- Artikelnr.: 61961457
- Verlag: Kogan Page Ltd
- Erscheinungstermin: 3. November 2023
- Englisch
- Abmessung: 238mm x 168mm x 29mm
- Gewicht: 830g
- ISBN-13: 9781398601710
- ISBN-10: 1398601713
- Artikelnr.: 61961457
Jan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.
Section
ONE: Introduction to Big Data; Chapter
01: Introduction to Big Data; Chapter
02: The Big Data framework; Chapter
03: Big Data strategy; Chapter
04: Big Data architecture; Chapter
05: Big Data algorithms; Chapter
06: Big Data processes; Chapter
07: Big Data functions; Chapter
08: Artificial intelligence; Section
TWO: Enterprise Big Data analysis; Chapter
09: Introduction to Big Data analysis; Chapter
10: Defining the business objective; Chapter
11: Data ingestion
importing and reading data sets; Chapter
12: Data preparation
cleaning and wrangling data; Chapter
13: Data analysis
model building; Chapter
14: Data presentation; Section
THREE: Enterprise Big Data engineering; Chapter
15: Introduction to Big Data engineering; Chapter
16: Data modelling; Chapter
17: Constructing the data lake; Chapter
18: Building an enterprise Big Data warehouse; Chapter
19: Design and structure of Big Data pipelines; Chapter
20: Managing data pipelines; Chapter
21: Cluster technology; Section
FOUR: enterprise Big Data algorithm design; Chapter
22: Introduction to Big Data algorithm design; Chapter
23: Algorithm design
fundamental concepts; Chapter
24: Statistical machine learning algorithms; Chapter
25: The data science roadmap; Chapter
26: Programming languages 26 visualization and simple metrics; Chapter
27: Advanced machine learning algorithms; Chapter
28: Advanced machine learning classification algorithms; Chapter
29: Technical communication and documentation; Section
FIVE: Enterprise Big Data architecture; Chapter
30: Introduction to the Big Data architecture; Chapter
31: Strength and resilience
the Big Data platform; Chapter
32: Design principles for Big Data architecture; Chapter
33: Big Data infrastructure; Chapter
34: Big Data platforms; Chapter
35: The Big Data application provider; Chapter
36: System orchestration in Big Data
ONE: Introduction to Big Data; Chapter
01: Introduction to Big Data; Chapter
02: The Big Data framework; Chapter
03: Big Data strategy; Chapter
04: Big Data architecture; Chapter
05: Big Data algorithms; Chapter
06: Big Data processes; Chapter
07: Big Data functions; Chapter
08: Artificial intelligence; Section
TWO: Enterprise Big Data analysis; Chapter
09: Introduction to Big Data analysis; Chapter
10: Defining the business objective; Chapter
11: Data ingestion
importing and reading data sets; Chapter
12: Data preparation
cleaning and wrangling data; Chapter
13: Data analysis
model building; Chapter
14: Data presentation; Section
THREE: Enterprise Big Data engineering; Chapter
15: Introduction to Big Data engineering; Chapter
16: Data modelling; Chapter
17: Constructing the data lake; Chapter
18: Building an enterprise Big Data warehouse; Chapter
19: Design and structure of Big Data pipelines; Chapter
20: Managing data pipelines; Chapter
21: Cluster technology; Section
FOUR: enterprise Big Data algorithm design; Chapter
22: Introduction to Big Data algorithm design; Chapter
23: Algorithm design
fundamental concepts; Chapter
24: Statistical machine learning algorithms; Chapter
25: The data science roadmap; Chapter
26: Programming languages 26 visualization and simple metrics; Chapter
27: Advanced machine learning algorithms; Chapter
28: Advanced machine learning classification algorithms; Chapter
29: Technical communication and documentation; Section
FIVE: Enterprise Big Data architecture; Chapter
30: Introduction to the Big Data architecture; Chapter
31: Strength and resilience
the Big Data platform; Chapter
32: Design principles for Big Data architecture; Chapter
33: Big Data infrastructure; Chapter
34: Big Data platforms; Chapter
35: The Big Data application provider; Chapter
36: System orchestration in Big Data
Section
ONE: Introduction to Big Data; Chapter
01: Introduction to Big Data; Chapter
02: The Big Data framework; Chapter
03: Big Data strategy; Chapter
04: Big Data architecture; Chapter
05: Big Data algorithms; Chapter
06: Big Data processes; Chapter
07: Big Data functions; Chapter
08: Artificial intelligence; Section
TWO: Enterprise Big Data analysis; Chapter
09: Introduction to Big Data analysis; Chapter
10: Defining the business objective; Chapter
11: Data ingestion
importing and reading data sets; Chapter
12: Data preparation
cleaning and wrangling data; Chapter
13: Data analysis
model building; Chapter
14: Data presentation; Section
THREE: Enterprise Big Data engineering; Chapter
15: Introduction to Big Data engineering; Chapter
16: Data modelling; Chapter
17: Constructing the data lake; Chapter
18: Building an enterprise Big Data warehouse; Chapter
19: Design and structure of Big Data pipelines; Chapter
20: Managing data pipelines; Chapter
21: Cluster technology; Section
FOUR: enterprise Big Data algorithm design; Chapter
22: Introduction to Big Data algorithm design; Chapter
23: Algorithm design
fundamental concepts; Chapter
24: Statistical machine learning algorithms; Chapter
25: The data science roadmap; Chapter
26: Programming languages 26 visualization and simple metrics; Chapter
27: Advanced machine learning algorithms; Chapter
28: Advanced machine learning classification algorithms; Chapter
29: Technical communication and documentation; Section
FIVE: Enterprise Big Data architecture; Chapter
30: Introduction to the Big Data architecture; Chapter
31: Strength and resilience
the Big Data platform; Chapter
32: Design principles for Big Data architecture; Chapter
33: Big Data infrastructure; Chapter
34: Big Data platforms; Chapter
35: The Big Data application provider; Chapter
36: System orchestration in Big Data
ONE: Introduction to Big Data; Chapter
01: Introduction to Big Data; Chapter
02: The Big Data framework; Chapter
03: Big Data strategy; Chapter
04: Big Data architecture; Chapter
05: Big Data algorithms; Chapter
06: Big Data processes; Chapter
07: Big Data functions; Chapter
08: Artificial intelligence; Section
TWO: Enterprise Big Data analysis; Chapter
09: Introduction to Big Data analysis; Chapter
10: Defining the business objective; Chapter
11: Data ingestion
importing and reading data sets; Chapter
12: Data preparation
cleaning and wrangling data; Chapter
13: Data analysis
model building; Chapter
14: Data presentation; Section
THREE: Enterprise Big Data engineering; Chapter
15: Introduction to Big Data engineering; Chapter
16: Data modelling; Chapter
17: Constructing the data lake; Chapter
18: Building an enterprise Big Data warehouse; Chapter
19: Design and structure of Big Data pipelines; Chapter
20: Managing data pipelines; Chapter
21: Cluster technology; Section
FOUR: enterprise Big Data algorithm design; Chapter
22: Introduction to Big Data algorithm design; Chapter
23: Algorithm design
fundamental concepts; Chapter
24: Statistical machine learning algorithms; Chapter
25: The data science roadmap; Chapter
26: Programming languages 26 visualization and simple metrics; Chapter
27: Advanced machine learning algorithms; Chapter
28: Advanced machine learning classification algorithms; Chapter
29: Technical communication and documentation; Section
FIVE: Enterprise Big Data architecture; Chapter
30: Introduction to the Big Data architecture; Chapter
31: Strength and resilience
the Big Data platform; Chapter
32: Design principles for Big Data architecture; Chapter
33: Big Data infrastructure; Chapter
34: Big Data platforms; Chapter
35: The Big Data application provider; Chapter
36: System orchestration in Big Data