Training Week: Introduction to Neo4j Bloom 2022Neo4j
This document provides an introduction and overview of Neo4j Bloom. It discusses how to set up Bloom, load sample data on the Winter Olympics, explore the data through visualizations and patterns, configure perspectives, add search phrases, and perform basic editing. Users are guided through exercises to become familiar with Bloom's capabilities and graph thinking. The document also provides resources for continuing one's graph database learning journey.
The future of vector databases looks promising as the need for efficient handling of high-dimensional data and similarity searches continues to grow across various domains, including machine learning, data science, recommendation systems, computer vision, natural language processing, and more.
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Boltdb - an embedded key value databaseManoj Awasthi
The document discusses BoltDB, an embedded key-value database written in Go. It describes how Tokopedia used BoltDB to store image mappings more efficiently than MongoDB. BoltDB provided fast retrieval, scalability to thousands of queries per second, and persistence without recomputing on startup. While not a traditional database, BoltDB's ACID semantics and ease of use made it suitable for Tokopedia's read-heavy use case of serving images from cached storage.
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015Chris Fregly
The document summarizes a presentation given by Chris Fregly on Project Tungsten and optimizations in Apache Spark. It discusses techniques like using off-heap memory, minimizing cache misses, and saturating I/O to sort 100 terabytes of data in Spark. The presentation also covered a recap of the "100TB GraySort challenge" where custom data structures and algorithms were used to optimize sorting and shuffling of data.
TFA Collector - what can one do with it Sandesh Rao
The document provides an overview of the Oracle Trace File Analyzer (TFA) features and capabilities. TFA is installed as part of Oracle Grid Infrastructure and Oracle Database installations and provides a single interface to collect diagnostic data across clusters and consolidate it in one place. It reduces the time required to obtain diagnostic data needed to diagnose problems, saving businesses money. TFA can automatically detect events, collect relevant diagnostics, notify administrators, and upload collections to Oracle Support.
Training Week: Introduction to Neo4j Bloom 2022Neo4j
This document provides an introduction and overview of Neo4j Bloom. It discusses how to set up Bloom, load sample data on the Winter Olympics, explore the data through visualizations and patterns, configure perspectives, add search phrases, and perform basic editing. Users are guided through exercises to become familiar with Bloom's capabilities and graph thinking. The document also provides resources for continuing one's graph database learning journey.
The future of vector databases looks promising as the need for efficient handling of high-dimensional data and similarity searches continues to grow across various domains, including machine learning, data science, recommendation systems, computer vision, natural language processing, and more.
Apache Sqoop Tutorial | Sqoop: Import & Export Data From MySQL To HDFS | Hado...Edureka!
** Hadoop Training: https://www.edureka.co/hadoop **
This Edureka PPT on Sqoop Tutorial will explain you the fundamentals of Apache Sqoop. It will also give you a brief idea on Sqoop Architecture. In the end, it will showcase a demo of data transfer between Mysql and Hadoop
Below topics are covered in this video:
1. Problems with RDBMS
2. Need for Apache Sqoop
3. Introduction to Sqoop
4. Apache Sqoop Architecture
5. Sqoop Commands
6. Demo to transfer data between Mysql and Hadoop
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Boltdb - an embedded key value databaseManoj Awasthi
The document discusses BoltDB, an embedded key-value database written in Go. It describes how Tokopedia used BoltDB to store image mappings more efficiently than MongoDB. BoltDB provided fast retrieval, scalability to thousands of queries per second, and persistence without recomputing on startup. While not a traditional database, BoltDB's ACID semantics and ease of use made it suitable for Tokopedia's read-heavy use case of serving images from cached storage.
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015Chris Fregly
The document summarizes a presentation given by Chris Fregly on Project Tungsten and optimizations in Apache Spark. It discusses techniques like using off-heap memory, minimizing cache misses, and saturating I/O to sort 100 terabytes of data in Spark. The presentation also covered a recap of the "100TB GraySort challenge" where custom data structures and algorithms were used to optimize sorting and shuffling of data.
TFA Collector - what can one do with it Sandesh Rao
The document provides an overview of the Oracle Trace File Analyzer (TFA) features and capabilities. TFA is installed as part of Oracle Grid Infrastructure and Oracle Database installations and provides a single interface to collect diagnostic data across clusters and consolidate it in one place. It reduces the time required to obtain diagnostic data needed to diagnose problems, saving businesses money. TFA can automatically detect events, collect relevant diagnostics, notify administrators, and upload collections to Oracle Support.
This document summarizes a benchmark study of file formats for Hadoop, including Avro, JSON, ORC, and Parquet. It found that ORC with zlib compression generally performed best for full table scans. However, Avro with Snappy compression worked better for datasets with many shared strings. The document recommends experimenting with the benchmarks, as performance can vary based on data characteristics and use cases like column projections.
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdfAltinity Ltd
Join the Altinity experts as we dig into ClickHouse sharding and replication, showing how they enable clusters that deliver fast queries over petabytes of data. We’ll start with basic definitions of each, then move to practical issues. This includes the setup of shards and replicas, defining schema, choosing sharding keys, loading data, and writing distributed queries. We’ll finish up with tips on performance optimization.
#ClickHouse #datasets #ClickHouseTutorial #opensource #ClickHouseCommunity #Altinity
-----------------
Join ClickHouse Meetups: https://www.meetup.com/San-Francisco-...
Check out more ClickHouse resources: https://altinity.com/resources/
Visit the Altinity Documentation site: https://docs.altinity.com/
Contribute to ClickHouse Knowledge Base: https://kb.altinity.com/
Join the ClickHouse Reddit community: https://www.reddit.com/r/Clickhouse/
----------------
Learn more about Altinity!
Site: https://www.altinity.com
LinkedIn: https://www.linkedin.com/company/alti...
Twitter: https://twitter.com/AltinityDB
Are your Oracle databases highly available? You have deployed Real Application Clusters (RAC), Data Guard, or Failover Clusters and are well protected against server failures? Great – the prerequisites for a highly available environment are given. However, to assure that backend infrastructure failures also remain transparent to the client, an appropriate configuration is a prerequisite.
This lecture will discuss the Oracle technologies that can be used to achieve automatic client failover functionality. What are the advantages, but also the limitations of these technologies?
[Container Plumbing Days 2023] Why was nerdctl made?Akihiro Suda
nerdctl (contaiNERD CTL) was made to facilitate development of new technologies in the containerd platform.
Such technologies include:
- Lazy-pulling with Stargz/Nydus/OverlayBD
- P2P image distribution with IPFS
- Image encryption with OCIcrypt
- Image signing with Cosign
- “Real” read-only mounts with mount_setattr
- Slirp-less rootless containers with bypass4netns
- Interactive debugging of Dockerfiles, with buildg
nerdctl is also useful for debugging Kubernetes nodes that are running containerd.
Through this session, the audiences will learn these functionalities of nerdctl, relevant projects, and the roadmap for the future.
https://containerplumbing.org/sessions/2023/why_was_nerdctl_
Big Data! Great! Now What? #SymfonyCon 2014Ricard Clau
Big Data is one of the new buzzwords in the industry. Everyone is using NoSQL databases. MySQL is not cool anymore. But... do we really have big data? Where should we store it? Are the traditional RDBMS databases dead? Is NoSQL the solution to our problems? And most importantly, how can PHP and Symfony2 help with it?
This presentation introduces various new features in Oracle's Universal Installer (OUI), configuration and related tools. This presentation was first presented in the course of the Oracle Database 2017 conference.
The document provides an introduction to Cassandra presented by Nick Bailey. It discusses key Cassandra concepts like cluster architecture, data modeling using CQL, and best practices. Examples are provided to illustrate how to model time-series data and denormalize schemas to support different queries. Tools for testing Cassandra implementations like CCM and client drivers are also mentioned.
High Performance, High Reliability Data Loading on ClickHouseAltinity Ltd
This document provides a summary of best practices for high reliability data loading in ClickHouse. It discusses ClickHouse's ingestion pipeline and strategies for improving performance and reliability of inserts. Some key points include using larger block sizes for inserts, avoiding overly frequent or compressed inserts, optimizing partitioning and sharding, and techniques like buffer tables and compact parts. The document also covers ways to make inserts atomic and handle deduplication of records through block-level and logical approaches.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.
This document discusses using ClickHouse for experimentation and metrics at Spotify. It describes how Spotify built an experimentation platform using ClickHouse to provide teams interactive queries on granular metrics data with low latency. Key aspects include ingesting data from Google Cloud Storage to ClickHouse daily, defining metrics through a centralized catalog, and visualizing metrics and running queries using Superset connected to ClickHouse. The platform aims to reduce load on notebooks and BigQuery by serving common queries directly from ClickHouse.
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
Introduction to Return-Oriented Exploitation on ARM64 - Billy EllisBillyEllis3
With the increasing number of ARM-based devices being used in the modern world today, mobile devices including tablets and smartphones are becoming a very worth-while target for attackers. This lecture will cover the fundamentals of both the ARM and ARM64 architectures, introduce return-oriented exploitation techniques for those who are unfamiliar and walk through the process of developing and executing an exploit on an ARM64-based system, making use of ROP and stack pivoting techniques along the way.
This document summarizes a benchmark study of file formats for Hadoop, including Avro, JSON, ORC, and Parquet. It found that ORC with zlib compression generally performed best for full table scans. However, Avro with Snappy compression worked better for datasets with many shared strings. The document recommends experimenting with the benchmarks, as performance can vary based on data characteristics and use cases like column projections.
Deep Dive on ClickHouse Sharding and Replication-2202-09-22.pdfAltinity Ltd
Join the Altinity experts as we dig into ClickHouse sharding and replication, showing how they enable clusters that deliver fast queries over petabytes of data. We’ll start with basic definitions of each, then move to practical issues. This includes the setup of shards and replicas, defining schema, choosing sharding keys, loading data, and writing distributed queries. We’ll finish up with tips on performance optimization.
#ClickHouse #datasets #ClickHouseTutorial #opensource #ClickHouseCommunity #Altinity
-----------------
Join ClickHouse Meetups: https://www.meetup.com/San-Francisco-...
Check out more ClickHouse resources: https://altinity.com/resources/
Visit the Altinity Documentation site: https://docs.altinity.com/
Contribute to ClickHouse Knowledge Base: https://kb.altinity.com/
Join the ClickHouse Reddit community: https://www.reddit.com/r/Clickhouse/
----------------
Learn more about Altinity!
Site: https://www.altinity.com
LinkedIn: https://www.linkedin.com/company/alti...
Twitter: https://twitter.com/AltinityDB
Are your Oracle databases highly available? You have deployed Real Application Clusters (RAC), Data Guard, or Failover Clusters and are well protected against server failures? Great – the prerequisites for a highly available environment are given. However, to assure that backend infrastructure failures also remain transparent to the client, an appropriate configuration is a prerequisite.
This lecture will discuss the Oracle technologies that can be used to achieve automatic client failover functionality. What are the advantages, but also the limitations of these technologies?
[Container Plumbing Days 2023] Why was nerdctl made?Akihiro Suda
nerdctl (contaiNERD CTL) was made to facilitate development of new technologies in the containerd platform.
Such technologies include:
- Lazy-pulling with Stargz/Nydus/OverlayBD
- P2P image distribution with IPFS
- Image encryption with OCIcrypt
- Image signing with Cosign
- “Real” read-only mounts with mount_setattr
- Slirp-less rootless containers with bypass4netns
- Interactive debugging of Dockerfiles, with buildg
nerdctl is also useful for debugging Kubernetes nodes that are running containerd.
Through this session, the audiences will learn these functionalities of nerdctl, relevant projects, and the roadmap for the future.
https://containerplumbing.org/sessions/2023/why_was_nerdctl_
Big Data! Great! Now What? #SymfonyCon 2014Ricard Clau
Big Data is one of the new buzzwords in the industry. Everyone is using NoSQL databases. MySQL is not cool anymore. But... do we really have big data? Where should we store it? Are the traditional RDBMS databases dead? Is NoSQL the solution to our problems? And most importantly, how can PHP and Symfony2 help with it?
This presentation introduces various new features in Oracle's Universal Installer (OUI), configuration and related tools. This presentation was first presented in the course of the Oracle Database 2017 conference.
The document provides an introduction to Cassandra presented by Nick Bailey. It discusses key Cassandra concepts like cluster architecture, data modeling using CQL, and best practices. Examples are provided to illustrate how to model time-series data and denormalize schemas to support different queries. Tools for testing Cassandra implementations like CCM and client drivers are also mentioned.
High Performance, High Reliability Data Loading on ClickHouseAltinity Ltd
This document provides a summary of best practices for high reliability data loading in ClickHouse. It discusses ClickHouse's ingestion pipeline and strategies for improving performance and reliability of inserts. Some key points include using larger block sizes for inserts, avoiding overly frequent or compressed inserts, optimizing partitioning and sharding, and techniques like buffer tables and compact parts. The document also covers ways to make inserts atomic and handle deduplication of records through block-level and logical approaches.
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop...Simplilearn
This presentation about Hadoop for beginners will help you understand what is Hadoop, why Hadoop, what is Hadoop HDFS, Hadoop MapReduce, Hadoop YARN, a use case of Hadoop and finally a demo on HDFS (Hadoop Distributed File System), MapReduce and YARN. Big Data is a massive amount of data which cannot be stored, processed, and analyzed using traditional systems. To overcome this problem, we use Hadoop. Hadoop is a framework which stores and handles Big Data in a distributed and parallel fashion. Hadoop overcomes the challenges of Big Data. Hadoop has three components HDFS, MapReduce, and YARN. HDFS is the storage unit of Hadoop, MapReduce is its processing unit, and YARN is the resource management unit of Hadoop. In this video, we will look into these units individually and also see a demo on each of these units.
Below topics are explained in this Hadoop presentation:
1. What is Hadoop
2. Why Hadoop
3. Big Data generation
4. Hadoop HDFS
5. Hadoop MapReduce
6. Hadoop YARN
7. Use of Hadoop
8. Demo on HDFS, MapReduce and YARN
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This talk delves into the many ways that a user has to use HBase in a project. Lars will look at many practical examples based on real applications in production, for example, on Facebook and eBay and the right approach for those wanting to find their own implementation. He will also discuss advanced concepts, such as counters, coprocessors and schema design.
This document discusses using ClickHouse for experimentation and metrics at Spotify. It describes how Spotify built an experimentation platform using ClickHouse to provide teams interactive queries on granular metrics data with low latency. Key aspects include ingesting data from Google Cloud Storage to ClickHouse daily, defining metrics through a centralized catalog, and visualizing metrics and running queries using Superset connected to ClickHouse. The platform aims to reduce load on notebooks and BigQuery by serving common queries directly from ClickHouse.
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
This document discusses big data, including what it is, common data sources, its volume, velocity and variety characteristics, solutions like Hadoop and its HDFS and MapReduce components, and the impact and future of big data. It explains that big data refers to large and complex datasets that are difficult to process using traditional tools. Hadoop provides a framework to store and process big data across clusters of commodity hardware.
Introduction to Return-Oriented Exploitation on ARM64 - Billy EllisBillyEllis3
With the increasing number of ARM-based devices being used in the modern world today, mobile devices including tablets and smartphones are becoming a very worth-while target for attackers. This lecture will cover the fundamentals of both the ARM and ARM64 architectures, introduce return-oriented exploitation techniques for those who are unfamiliar and walk through the process of developing and executing an exploit on an ARM64-based system, making use of ROP and stack pivoting techniques along the way.
This document discusses data management and reporting processes. It involves moving raw data through various staging areas and vaults before being transformed and loaded into data marts and databases. The stages include cleansing, mapping, and automating extraction, transformation, and loading of data into systems using SQL views and bridges.
Präsentation auf der DOAG Konferenz
Metadaten sind ein häufig vernachlässigtes Thema, da Metadaten als langweilig betrachtet oder auch nicht bewusst wahr genommen werden. Auch die eher abstrakten Beschreibungen wie "Metadaten sind Daten über Daten" sind nicht gerade hilfreich.
In der Präsentation werden die verschiedenen Arten von Metadaten (fachlich, technisch, prozessual) besprochen. Es wird darauf eingegangen, wie diese in einem Data Vault Projekt genutzt wurden, um z.B. Vorgaben festzulegen oder Code zu generieren.
The document introduces Visual DataVault, a modeling language for visually expressing Data Vault models. It aims to generate DDL from models and support Microsoft Office. The language defines basic entities like hubs, links, satellites and reference tables. It also covers query assistant tables, computed structures, exploration links and business vault tables to enhance the raw data vault. Some remarks note it focuses on logical not physical modeling and more features are planned.
Data Vault 2.0: Using MD5 Hashes for Change Data CaptureKent Graziano
This presentation was given at OakTable World 2014 (#OTW14) in San Francisco as a short Ted-style 10 minute talk. In it I introduce Data Vault 2.0 and its innovative approach to doing change data capture in a data warehouse by using MD5 Hash columns.
Enabling AgileBI by managing the data warehouse software lifecycle with DataVault 2.0, generators, data virtualization and cotinuous integration using open source tools
Not to be confused with Oracle Database Vault (a commercial db security product), Data Vault Modeling is a specific data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for the last 10 years but is still not widely known or understood. The purpose of this presentation is to provide attendees with a detailed introduction to the technical components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics for how to build, and design structures when using the Data Vault modeling technique. The target audience is anyone wishing to explore implementing a Data Vault style data model for an Enterprise Data Warehouse, Operational Data Warehouse, or Dynamic Data Integration Store. See more content like this by following my blog http://kentgraziano.com or follow me on twitter @kentgraziano.
7. 3FolieFolieAWF Arbeitsgemeinschaft “Pull-Systeme” – Dipl.-Ing. O. Völker und Dipl.-Ing. S. Binner
Einleitung „Push“ und „Pull“
In OutBestand in der Fertigung
Ziehlogik (PullZiehlogik (Pull--Prinzip)Prinzip)
Bestand in der Fertigung
In Out
Schiebelogik (PushSchiebelogik (Push--Prinzip)Prinzip)
10. I
•Single Version of Facts
II
•Multiple Versions of Truth
III
• Single
Sources
IV
• All Data
MPP
Automatisierung
des DWH mit
DataVault
Enterprise Information Products
Reports
Predictive Analytics
Adhoc-QueriesDWH Mart
Data LakeInput
ComplicatedSimple
Chaotic
Analytics, Innovations
Data Science
Data Mining
Machine Learning
Alle Daten
Complex
11. Manuelles ETL
Bereinigung
Geschäftsregeln
Datenmodell getriebene Automation
Integration nach Business Key
(fachlich)
Historisierung
Moderne DWH Architektur mit Data
Vault
I
• Facts
II
• Context
III
• Shadow IT
IV
• Analytics, Research, Prototyping
Raw Vault
“Single Version of
Facts”
Business
Vault
Source Stage
Report
Mart
“Multiple
Versions of
Truth”
13. Ladestrecken - Hub
SELECT
DISTINCT
BK
Erstelle SK
Im Ziel
vorhanden
?
Lookup
INSERT INTO
Hub
Stage
Raw
Vault
SELECT
DISTINCT
BK
WHERE NOT EXISTS IN Hub
Erstelle SK
INSERT INTO
Hub
Stage
Raw
Vault
SELECT
DISTINCT
BK, MD5
WHERE NOT EXISTS IN Hub
INSERT INTO
Hub
Stage
Raw
Vault
INSERT INTO HUB
SELECT
DISTINCT
BK, MD5
WHERE NOT EXISTS IN Hub
Stage
Raw
Vault
Ja
Nein
14. Ladestrecken - Link
SELECT
DISTINCT
Liste der BKs
Erstelle SK
Im Ziel
vorhanden
?
Lookup
INSERT INTO
Link
Stage
Raw
Vault
Ja
Nein
Lookup SK 1
Lookup SK 2
Lookup SK n
?
?
?
15. Ladestrecken - Link
SELECT
DISTINCT
List der BK
Erstelle SK
Im Ziel
vorhanden
?
Lookup
INSERT INTO
Link
Stage
Raw
Vault
Ja
Nein
Erstelle SK
pro BK
SELECT
DISTINCT
List der BK,MD5
WHERE NOT EXISTS IN Link Erstelle SK
INSERT INTO
Link
Stage
Raw
Vault
18. MD5
• Message-digest Algorithm 128-bit (16-byte) oder 32 digit
hexadecimal
• Ronald Rivest in 1991
• RFC-1321
• Collision durch Präparation der Eingabgedateien erzwingbar
• Algorithmus zur Berechnung im Data-Vault muss eingehalten
werden!
– NULL-Handling
– Formate für Zahlen und Datum
– Trennzeichen!
• Alternativen: http://en.wikipedia.org/wiki/List_of_hash_functions
19. Vielen Dank für Ihre Aufmerksamkeit!
Fragen?
tglunde
Torsten Glunde
mailto:t.glunde(at)alligator-company.de
Weitere Netzwerke:
https://www.xing.com/profile/Torsten_Glunde
https://www.linkedin.com/pub/torsten-glunde/8/aba/97
22. I
• Facts
II
• Context
III
• Shadow IT
IV
• Analytics, Research, Prototyping
Raw Vault
Business
Vault
Source Stage
Conceptional Data Model
Report
Mart
PDM
LDM
Sync
Sync
Data Flow
Stage
Tables
Map 1:1 Map F(x) F(x) Map