Mark Needham – författare & böcker

Building Real-Time Analytics Systems

From Events to Insights with Apache Kafka and Apache Pinot

E-bok

Engelska, 2023

627 kr

Läs direkt efter köp

Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly.

Author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The book''s second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service.

You will:

Learn common architectures for real-time analyticsDiscover how event processing differs from real-time analyticsIngest event data from Apache Kafka into Apache PinotCombine event streams with OLTP data using Debezium and Kafka StreamsWrite real-time queries against event data stored in Apache PinotBuild a real-time dashboard and order tracking appLearn how Uber, Stripe, and Just Eat use real-time analytics

Building Real-Time Analytics Systems

AvMark Needham

E-bok

PDF, Engelska, 2023

627 kr

Läs direkt efter köp

Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly.

Author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The book''s second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service.

You will:

Learn common architectures for real-time analyticsDiscover how event processing differs from real-time analyticsIngest event data from Apache Kafka into Apache PinotCombine event streams with OLTP data using Debezium and Kafka StreamsWrite real-time queries against event data stored in Apache PinotBuild a real-time dashboard and order tracking appLearn how Uber, Stripe, and Just Eat use real-time analytics

Building Real-Time Analytics Systems

From Events to Insights with Apache Kafka and Apache Pinot

AvMark Needham

Häftad, Engelska, 2023

502 kr

Skickas inom 5-8 vardagar

Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly.In the first part of this book, author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service.With this book, you will:Learn common architectures for real-time analyticsDiscover how event processing differs from real-time analyticsIngest event data from Apache Kafka into Apache PinotCombine event streams with static data using Kafka StreamsWrite real-time queries against event data stored in Apache PinotBuild a real-time dashboard, fraud detection pipeline, order tracking app, and anomaly detection systemLearn how organizations like Uber, Stripe, and Just Eat use real-time analytics

Graph Algorithms

Practical Examples in Apache Spark and Neo4j

AvAmy E. Hodler,Mark Needham

E-bok

Engelska, 2019

712 kr

Läs direkt efter köp

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.

This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection.

Learn how graph analytics vary from conventional statistical analysisUnderstand how classic graph algorithms work, and how they are appliedGet guidance on which algorithms to use for different types of questionsExplore algorithm examples with working code and sample datasets from Spark and Neo4jSee how connected feature extraction can increase machine learning accuracy and precisionWalk through creating an ML workflow for link prediction combining Neo4j and Spark

Graph Algorithms

Practical Examples in Apache Spark and Neo4j

AvAmy E. Hodler,Mark Needham

E-bok

PDF, Engelska, 2019

712 kr

Läs direkt efter köp

Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.

This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection.

Learn how graph analytics vary from conventional statistical analysisUnderstand how classic graph algorithms work, and how they are appliedGet guidance on which algorithms to use for different types of questionsExplore algorithm examples with working code and sample datasets from Spark and Neo4jSee how connected feature extraction can increase machine learning accuracy and precisionWalk through creating an ML workflow for link prediction combining Neo4j and Spark

Graph Algorithms

Practical Examples in Apache Spark and Neo4j

AvAmy Hodler,Mark Needham

Häftad, Engelska, 2019

607 kr

Skickas inom 5-8 vardagar

Learn how graph algorithms can help you leverage relationships within your data to develop intelligent solutions and enhance your machine learning models. With this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they’re used for building dynamic network models or forecasting real-world behavior. Mark Needham and Amy Hodler from Neo4j explain how graph algorithms describe complex structures and reveal difficult-to-find patterns—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. You’ll walk through hands-on examples that show you how to use graph algorithms in Apache Spark and Neo4j, two of the most common choices for graph analytics. Learn how graph analytics reveal more predictive elements in today’s data Understand how popular graph algorithms work and how they’re applied Use sample code and tips from more than 20 graph algorithm examples Learn which algorithms to use for different types of questions Explore examples with working code and sample datasets for Spark and Neo4j Create an ML workflow for link prediction by combining Neo4j and Spark

DuckDB in Action

AvMark Needham,Michael Hungerm. fl.

Inbunden, Engelska, 2024

429 kr

Skickas inom 7-10 vardagar

Dive into DuckDB and start processing gigabytes of data with ease—all with no data warehouse. DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you'll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you'll learn everything you need to get the most out of DuckDB—all through hands-on examples. Open up DuckDB in Action and learn how to: Read and process data from CSV, JSON and Parquet sources both locally and remote Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames Prepare, ingest and query large datasets Build cloud data pipelines Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won't need to read through pages of documentation—you'll learn as you work. Get to grips with DuckDB's unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. About the technology DuckDB makes data analytics fast and fun! You don't need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres.

DuckDB in Action

AvMichael Simons,Michael Hungerm. fl.

E-bok

Engelska, 2024

467 kr

Läs direkt efter köp

Dive into DuckDB and start processing gigabytes of data with ease—all with no data warehouse.DuckDB is a cutting-edge SQL database that makes it incredibly easy to analyze big data sets right from your laptop. In DuckDB in Action you’ll learn everything you need to know to get the most out of this awesome tool, keep your data secure on prem, and save you hundreds on your cloud bill. From data ingestion to advanced data pipelines, you’ll learn everything you need to get the most out of DuckDB—all through hands-on examples. Open up DuckDB in Action and learn how to: • Read and process data from CSV, JSON and Parquet sources both locally and remote • Write analytical SQL queries, including aggregations, common table expressions, window functions, special types of joins, and pivot tables • Use DuckDB from Python, both with SQL and its "Relational"-API, interacting with databases but also data frames • Prepare, ingest and query large datasets • Build cloud data pipelines • Extend DuckDB with custom functionality Pragmatic and comprehensive, DuckDB in Action introduces the DuckDB database and shows you how to use it to solve common data workflow problems. You won’t need to read through pages of documentation—you’ll learn as you work. Get to grips with DuckDB''s unique SQL dialect, learning to seamlessly load, prepare, and analyze data using SQL queries. Extend DuckDB with both Python and built-in tools such as MotherDuck, and gain practical insights into building robust and automated data pipelines. About the technology DuckDB makes data analytics fast and fun! You don’t need to set up a Spark or run a cloud data warehouse just to process a few hundred gigabytes of data. DuckDB is easily embeddable in any data analytics application, runs on a laptop, and processes data from almost any source, including JSON, CSV, Parquet, SQLite and Postgres. About the book DuckDB in Action guides you example-by-example from setup, through your first SQL query, to advanced topics like building data pipelines and embedding DuckDB as a local data store for a Streamlit web app. You’ll explore DuckDB’s handy SQL extensions, get to grips with aggregation, analysis, and data without persistence, and use Python to customize DuckDB. A hands-on project accompanies each new topic, so you can see DuckDB in action. What''s inside • Prepare, ingest and query large datasets • Build cloud data pipelines • Extend DuckDB with custom functionality • Fast-paced SQL recap: From simple queries to advanced analytics About the reader For data pros comfortable with Python and CLI tools. About the author Mark Needham is a blogger and video creator at @?LearnDataWithMark. Michael Hunger leads product innovation for the Neo4j graph database. Michael Simons is a Java Champion, author, and Engineer at Neo4j.

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Mark Needham – författare

Building Real-Time Analytics Systems

Building Real-Time Analytics Systems

Building Real-Time Analytics Systems

Graph Algorithms

Graph Algorithms

Graph Algorithms

DuckDB in Action

DuckDB in Action

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Mina sidor

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Om Bokus

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