Michael Hunger – författare

You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop.

Through several sample projects, you’ll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. You’ll also discover the features Spring Data adds to Spring’s existing JPA and JDBC support for writing RDBMS-based data access layers.

Learn about Spring’s template helper classes to simplify the use ofdatabase-specific functionalityExplore Spring Data’s repository abstraction and advanced query functionalityUse Spring Data with Redis (key/value store), HBase(column-family), MongoDB (document database), and Neo4j (graph database)Discover the GemFire distributed data grid solutionExport Spring Data JPA-managed entities to the Web as RESTful web servicesSimplify the development of HBase applications, using a lightweight object-mapping frameworkBuild example big-data pipelines with Spring Batch and Spring Integration

You can choose several data access frameworks when building Java enterprise applications that work with relational databases. But what about big data? This hands-on introduction shows you how Spring Data makes it relatively easy to build applications across a wide range of new data access technologies such as NoSQL and Hadoop.

Through several sample projects, you’ll learn how Spring Data provides a consistent programming model that retains NoSQL-specific features and capabilities, and helps you develop Hadoop applications across a wide range of use-cases such as data analysis, event stream processing, and workflow. You’ll also discover the features Spring Data adds to Spring’s existing JPA and JDBC support for writing RDBMS-based data access layers.

Learn about Spring’s template helper classes to simplify the use ofdatabase-specific functionalityExplore Spring Data’s repository abstraction and advanced query functionalityUse Spring Data with Redis (key/value store), HBase(column-family), MongoDB (document database), and Neo4j (graph database)Discover the GemFire distributed data grid solutionExport Spring Data JPA-managed entities to the Web as RESTful web servicesSimplify the development of HBase applications, using a lightweight object-mapping frameworkBuild example big-data pipelines with Spring Batch and Spring Integration

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.

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.

Der Mehrwert von Informationen steckt nicht notwendigerweise in der reinen Menge von Daten, sondern vor allem in den Beziehungenzwischen Elementen. Bisher war es nicht so einfach, die interessanten, vernetzten Domänen verlustlos in einer Datenbank zuspeichern und effizient abzufragen. Neo4j, besonders die neue Version 2.0, erlaubt es, komplexe Datenmodelle direkt zu modellierenund herausfordernde Anwendungsfälle performant umzusetzen. In diesem Buch gibt Michael Hunger, langjähriger Mitarbeiter von NeoTechnology, an praktischen Beispielen einen Überblick zur Anwendung der quelloffenen Graphdatenbank.

NoSQL führt eine Vielzahl neuer Technologien in die Datenbankszene ein. Das bedeutet mehr Verantwortung für den Einzelnen, sich umfassend zu informieren. Hierzu bietetdas erste Kapitel des shortcuts eine überblicksartige Einführung, der im Folgenden die unterschiedlichen Datenbanktechnologien erläutert. Von all den Informationen, die täglich verarbeitet werden, ist ein beträchtlicher Anteil nicht wegen des Umfangs interessant, sondern wegen der inhärenten Verknüpfungen. Der effektiven Handhabung vernetzter Daten mit Neo4j widmet sich das zweite Kapitel. Horizontale Skalierung und hoher Durchsatz sind die großen Stärken von Apache Cassandra, das im Zentrum des dritten Kapitels steht. Mit seinen Wurzeln in der BigTable-Technologie hat sich HBase über die Jahre zur Grundlage vieler Google-Produkte und -Dienste entwickelt. Welche Ideen, Architektur und welches Datenmodell stecken hinter HBase? Mit diesen Fragen beschäftigt sich abschließend Kapitel 4 des shortcuts.