This document discusses big data analytics in a heterogeneous world. It covers the issues of dealing with volume, variety and velocity of big data. It also discusses the growing trends in big data analytics solutions including NoSQL databases, Hadoop, columnar databases and in-memory analytics. Finally, it proposes a comprehensive three-tier framework using commercial and open source software to provide reliable data management, application services and business intelligence tools to build bridges across heterogeneous data environments.
A Basic Introduction to the Hadoop eco system - no animationSameer Tiwari
The document provides a basic introduction to the Hadoop ecosystem. It describes the key components which include HDFS for raw storage, HBase for columnar storage, Hive and Pig as query engines, MapReduce and YARN as schedulers, Flume for streaming, Mahout for machine learning, Oozie for workflows, and Zookeeper for distributed locking. Each component is briefly explained including their goals, architecture, and how they relate to and build upon each other.
The document provides an introduction to big data and Apache Hadoop. It discusses big data concepts like the 3Vs of volume, variety and velocity. It then describes Apache Hadoop including its core architecture, HDFS, MapReduce and running jobs. Examples of using Hadoop for a retail system and with SQL Server are presented. Real world applications at Microsoft and case studies are reviewed. References for further reading are included at the end.
Big data and Hadoop are introduced as ways to handle the increasing volume, variety, and velocity of data. Hadoop evolved as a solution to process large amounts of unstructured and semi-structured data across distributed systems in a cost-effective way using commodity hardware. It provides scalable and parallel processing via MapReduce and HDFS distributed file system that stores data across clusters and provides redundancy and failover. Key Hadoop projects include HDFS, MapReduce, HBase, Hive, Pig and Zookeeper.
This document introduces Cassandra and Hadoop and how they can be used together for analytics over Cassandra data. It discusses how Cassandra is good for writes and random reads at scale but not ad-hoc queries, while Hadoop tools like MapReduce, Pig, and Hive can query Cassandra data and are extensible. It provides examples of using MapReduce and Pig with Cassandra and discusses how Raptr.com uses Cassandra and Hadoop together to improve query performance from hours to 10-15 minutes.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its Hadoop Distributed File System (HDFS) and allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop was created by Doug Cutting and Mike Cafarella to address the growing need to handle large datasets in a distributed computing environment.
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
A Basic Introduction to the Hadoop eco system - no animationSameer Tiwari
The document provides a basic introduction to the Hadoop ecosystem. It describes the key components which include HDFS for raw storage, HBase for columnar storage, Hive and Pig as query engines, MapReduce and YARN as schedulers, Flume for streaming, Mahout for machine learning, Oozie for workflows, and Zookeeper for distributed locking. Each component is briefly explained including their goals, architecture, and how they relate to and build upon each other.
The document provides an introduction to big data and Apache Hadoop. It discusses big data concepts like the 3Vs of volume, variety and velocity. It then describes Apache Hadoop including its core architecture, HDFS, MapReduce and running jobs. Examples of using Hadoop for a retail system and with SQL Server are presented. Real world applications at Microsoft and case studies are reviewed. References for further reading are included at the end.
Big data and Hadoop are introduced as ways to handle the increasing volume, variety, and velocity of data. Hadoop evolved as a solution to process large amounts of unstructured and semi-structured data across distributed systems in a cost-effective way using commodity hardware. It provides scalable and parallel processing via MapReduce and HDFS distributed file system that stores data across clusters and provides redundancy and failover. Key Hadoop projects include HDFS, MapReduce, HBase, Hive, Pig and Zookeeper.
This document introduces Cassandra and Hadoop and how they can be used together for analytics over Cassandra data. It discusses how Cassandra is good for writes and random reads at scale but not ad-hoc queries, while Hadoop tools like MapReduce, Pig, and Hive can query Cassandra data and are extensible. It provides examples of using MapReduce and Pig with Cassandra and discusses how Raptr.com uses Cassandra and Hadoop together to improve query performance from hours to 10-15 minutes.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It provides reliable storage through its Hadoop Distributed File System (HDFS) and allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop was created by Doug Cutting and Mike Cafarella to address the growing need to handle large datasets in a distributed computing environment.
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 document summarizes the history and evolution of Apache Hadoop over the past 10 years. It discusses how Hadoop originated from Doug Cutting's work on Nutch in 2002. It grew to include HDFS for storage and MapReduce for processing. Yahoo was an early large-scale user. The community has expanded Hadoop to include over 25 components like Hive, HBase, Spark and more. The open source model and ability to adapt have helped Hadoop succeed and it will continue to evolve to handle new data sources and cloud deployments in the next 10 years.
The document discusses big data and Hadoop, providing an introduction to big data, use cases across industries, an overview of the Hadoop ecosystem and architecture, and learning paths for professionals. It also includes examples of how companies like Facebook use large Hadoop clusters to store and process massive amounts of user data at petabyte scale. The presentation aims to help attendees understand big data, Hadoop, and career opportunities working with these technologies.
End-to-end Analytics with Apache CassandraJeremy Hanna
The document discusses using Apache Cassandra for end-to-end analytics. It provides an overview of Cassandra's capabilities for analytics like Pig and Hive integration, and recommends use cases like trends analysis, data problem detection, and backpopulating historical data. It also provides tips on data modeling, output formats, and integrating Cassandra with tools like Oozie, Pig, and Hadoop distributions.
Apache Hadoop is an open source framework that allows you to process large data sets (a.k.a Big Data) across clusters using simple programming models. This TechTalk will introduce you to real-life usages of Hadoop, so you can better understand when to use it, as well as describing its components and the first steps to setup a Hadoop cluster.
By Dina Abu Khader - System Administrator
YouTube video: http://www.youtube.com/watch?v=pSjP171i-gM
The document is an introduction to big data and Hadoop that discusses:
1) What big data is and common use cases across different industries.
2) The characteristics of big data according to IBM.
3) An overview of the Hadoop ecosystem including HDFS, MapReduce, YARN and other related frameworks.
4) How Hadoop allows for distributed processing of large datasets across clusters of machines more efficiently than traditional systems.
The document provides an overview of Redis Modules, which allow Redis to be extended through dynamically loaded libraries written in C. Some key modules discussed include ReJSON for storing and querying JSON documents natively in Redis, RediSearch for full-text search capabilities, and ReBloom for implementing scalable Bloom filters. Redis Modules can be used to add new data types, commands, and capabilities to Redis in order to adapt it to specific use cases and data models. Performance benchmarks show modules like ReJSON providing significant performance advantages over alternatives that rely on Redis' core data structures and Lua scripting.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
Content presented at a talk on Aug. 29th. Purpose is to inform a fairly technical audience on the primary tenets of Big Data and the hadoop stack. Also, did a walk-thru' of hadoop and some of the hadoop stack i.e. Pig, Hive, Hbase.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for the storage and analysis of datasets that are too large for single servers. The document discusses several key Hadoop components including HDFS for storage, MapReduce for processing, HBase for column-oriented storage, Hive for SQL-like queries, Pig for data flows, and Sqoop for data transfer between Hadoop and relational databases. It provides examples of how each component can be used and notes that Hadoop is well-suited for large-scale batch processing of data.
This document discusses Big Data and Hadoop. It begins with prerequisites for Hadoop including Java, OOP concepts, and data structures. It then defines Big Data as being on the order of petabytes, far larger than typical files. Hadoop provides a solution for storing, processing, and analyzing this large data across clusters of commodity hardware using its HDFS distributed file system and MapReduce processing paradigm. A case study demonstrates how Hadoop can help a telecom company analyze usage data from millions of subscribers to improve service offerings.
This document provides an overview of Hadoop and its ecosystem. It describes Hadoop as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage, and MapReduce as a programming model for distributed computation across large datasets. A variety of related projects form the Hadoop ecosystem, providing capabilities like data integration, analytics, workflow scheduling and more.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems with traditional systems like data growth, network/server failures, and high costs by allowing data to be stored in a distributed manner and processed in parallel. Hadoop has two main components - the Hadoop Distributed File System (HDFS) which provides high-throughput access to application data across servers, and the MapReduce programming model which processes large amounts of data in parallel by splitting work into map and reduce tasks.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
Büyük Veri, Küme Hesaplama, Dağıtık Dosya Sistemi, Yüksek Performanslı Kümeleme, Apache Spark ve Streaming Modülünü içeren bir sunum.
Apache Spark’ın küme hesaplamaları için kullanımının anlatıldığı sunumda, Java API ile temel bir uygulama örneği gösteriliyor ve beraberinde gelen “Streaming Modülü” ile Twitter’dan canlı veri çekerek işlenmesi anlatılıyor.
Vodafone, Cyberpark ve Türkiye Teknoloji Geliştirme Vakfı işbirliğinde düzenlen etkinlikte büyük veri kavramı, Apache Hadoop Ekosistemi ve Türkiye ve Dünyadaki örnek uygulamalar anlatıldı.
-
1 Haziran 2016 - Onur Karadeli, Mustafa Murat Sever
This document summarizes the history and evolution of Apache Hadoop over the past 10 years. It discusses how Hadoop originated from Doug Cutting's work on Nutch in 2002. It grew to include HDFS for storage and MapReduce for processing. Yahoo was an early large-scale user. The community has expanded Hadoop to include over 25 components like Hive, HBase, Spark and more. The open source model and ability to adapt have helped Hadoop succeed and it will continue to evolve to handle new data sources and cloud deployments in the next 10 years.
The document discusses big data and Hadoop, providing an introduction to big data, use cases across industries, an overview of the Hadoop ecosystem and architecture, and learning paths for professionals. It also includes examples of how companies like Facebook use large Hadoop clusters to store and process massive amounts of user data at petabyte scale. The presentation aims to help attendees understand big data, Hadoop, and career opportunities working with these technologies.
End-to-end Analytics with Apache CassandraJeremy Hanna
The document discusses using Apache Cassandra for end-to-end analytics. It provides an overview of Cassandra's capabilities for analytics like Pig and Hive integration, and recommends use cases like trends analysis, data problem detection, and backpopulating historical data. It also provides tips on data modeling, output formats, and integrating Cassandra with tools like Oozie, Pig, and Hadoop distributions.
Apache Hadoop is an open source framework that allows you to process large data sets (a.k.a Big Data) across clusters using simple programming models. This TechTalk will introduce you to real-life usages of Hadoop, so you can better understand when to use it, as well as describing its components and the first steps to setup a Hadoop cluster.
By Dina Abu Khader - System Administrator
YouTube video: http://www.youtube.com/watch?v=pSjP171i-gM
The document is an introduction to big data and Hadoop that discusses:
1) What big data is and common use cases across different industries.
2) The characteristics of big data according to IBM.
3) An overview of the Hadoop ecosystem including HDFS, MapReduce, YARN and other related frameworks.
4) How Hadoop allows for distributed processing of large datasets across clusters of machines more efficiently than traditional systems.
The document provides an overview of Redis Modules, which allow Redis to be extended through dynamically loaded libraries written in C. Some key modules discussed include ReJSON for storing and querying JSON documents natively in Redis, RediSearch for full-text search capabilities, and ReBloom for implementing scalable Bloom filters. Redis Modules can be used to add new data types, commands, and capabilities to Redis in order to adapt it to specific use cases and data models. Performance benchmarks show modules like ReJSON providing significant performance advantages over alternatives that rely on Redis' core data structures and Lua scripting.
- Hadoop is a framework for managing and processing big data distributed across clusters of computers. It allows for parallel processing of large datasets.
- Big data comes from various sources like customer behavior, machine data from sensors, etc. It is used by companies to better understand customers and target ads.
- Hadoop uses a master-slave architecture with a NameNode master and DataNode slaves. Files are divided into blocks and replicated across DataNodes for reliability. The NameNode tracks where data blocks are stored.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses challenges in big data by providing reliability, scalability, and fault tolerance. Hadoop allows distributed processing of large datasets across clusters using MapReduce and can scale from single servers to thousands of machines, each offering local computation and storage. It is widely used for applications such as log analysis, data warehousing, and web indexing.
The document provides an overview of Big Data technology landscape, specifically focusing on NoSQL databases and Hadoop. It defines NoSQL as a non-relational database used for dealing with big data. It describes four main types of NoSQL databases - key-value stores, document databases, column-oriented databases, and graph databases - and provides examples of databases that fall under each type. It also discusses why NoSQL and Hadoop are useful technologies for storing and processing big data, how they work, and how companies are using them.
Content presented at a talk on Aug. 29th. Purpose is to inform a fairly technical audience on the primary tenets of Big Data and the hadoop stack. Also, did a walk-thru' of hadoop and some of the hadoop stack i.e. Pig, Hive, Hbase.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Hadoop is an open-source framework for storing and processing large datasets in a distributed computing environment. It allows for the storage and analysis of datasets that are too large for single servers. The document discusses several key Hadoop components including HDFS for storage, MapReduce for processing, HBase for column-oriented storage, Hive for SQL-like queries, Pig for data flows, and Sqoop for data transfer between Hadoop and relational databases. It provides examples of how each component can be used and notes that Hadoop is well-suited for large-scale batch processing of data.
This document discusses Big Data and Hadoop. It begins with prerequisites for Hadoop including Java, OOP concepts, and data structures. It then defines Big Data as being on the order of petabytes, far larger than typical files. Hadoop provides a solution for storing, processing, and analyzing this large data across clusters of commodity hardware using its HDFS distributed file system and MapReduce processing paradigm. A case study demonstrates how Hadoop can help a telecom company analyze usage data from millions of subscribers to improve service offerings.
This document provides an overview of Hadoop and its ecosystem. It describes Hadoop as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. The key components of Hadoop are the Hadoop Distributed File System (HDFS) for storage, and MapReduce as a programming model for distributed computation across large datasets. A variety of related projects form the Hadoop ecosystem, providing capabilities like data integration, analytics, workflow scheduling and more.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems with traditional systems like data growth, network/server failures, and high costs by allowing data to be stored in a distributed manner and processed in parallel. Hadoop has two main components - the Hadoop Distributed File System (HDFS) which provides high-throughput access to application data across servers, and the MapReduce programming model which processes large amounts of data in parallel by splitting work into map and reduce tasks.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Hadoop 2.0 architecture uses a scale-out storage and distributed processing framework. It stores large datasets across commodity hardware clusters and allows for processing using a simple programming model. The architecture utilizes HDFS for storage which splits files into 128MB blocks, stores replicas across racks for fault tolerance, and is managed by a resource manager and node managers that track hardware resources and heartbeats.
Büyük Veri, Küme Hesaplama, Dağıtık Dosya Sistemi, Yüksek Performanslı Kümeleme, Apache Spark ve Streaming Modülünü içeren bir sunum.
Apache Spark’ın küme hesaplamaları için kullanımının anlatıldığı sunumda, Java API ile temel bir uygulama örneği gösteriliyor ve beraberinde gelen “Streaming Modülü” ile Twitter’dan canlı veri çekerek işlenmesi anlatılıyor.
Vodafone, Cyberpark ve Türkiye Teknoloji Geliştirme Vakfı işbirliğinde düzenlen etkinlikte büyük veri kavramı, Apache Hadoop Ekosistemi ve Türkiye ve Dünyadaki örnek uygulamalar anlatıldı.
-
1 Haziran 2016 - Onur Karadeli, Mustafa Murat Sever
Gelişen enformatik teknolojisinin olanak sağladığı veri depolama kapasitesinin konvansiyonel tekniklerle stratejik bilgiye dönüştürülemediği yaygın olarak paylaşılan bir gerçek. ASO Dergisi Aralık Sayısında yayınlanan bu çalışma dosyası matematiğin ve istatistiğin ötesinde bu verilerin bilgiye dönüştürülmesi ve risk yönetiminde kullanılması olanaklarını araştırıyor.
APT (Advanced Persistent Threat - Gelişmiş Devamlı Tehdit) saldırıları konusunda düzenlediğim seminere ait sunumun bir kısmıdır. Sunum içerisinde yer alan konu başlıkları buradadır, sunumun tamamını dağıtımını kontrol edebilmek ve içerisindeki hassas bilgilerin korunması amacıyla paylaşmıyorum.
This document appears to be notes from a meeting between two individuals, Belkis Ozhorasan from Microsoft and Koray Kocabaş from Yemek Sepeti, discussing Microsoft's HDInsight Service on Windows Azure and how it provides on-demand, dedicated Hadoop clusters in the cloud.
15 Aralık 2016, Mef Üniversitesi Büyük Veri Analitiği yüksek lisans dersinde konuk konuşmacı olarak anlattığım IoT ve Data konusu. Microsoft'un bu konulardaki vizyonu, lokal örnekler ve başarılı kullanım senaryolarını konuştuğumuz slaytlar ekteki gibidir.
https://twitter.com/ikivanc
This document provides an overview of big data, Hadoop ecosystem, and data science. It discusses key concepts like what big data is, different types of big data, evolution of big data technologies, components of Hadoop ecosystem like MapReduce, HDFS, HBase, components for data ingestion and analytics. It also summarizes common techniques used in data science like descriptive analytics, predictive analytics, prescriptive analytics, and provides examples of exploratory data analysis and data mining.
Günümüz dünyasında “performansın zaman metriği değişmiştir, aynı zamanda performans düzeyi de artmıştır”. Dolayısıyla gerçek zamanlı bir analizden söz ediliyorsa firmanın yarattığı gerçek değerin ölçülmesine ve görselleştirilmesine olanak sağlayacak Grafik DataMining tekniğine yoğunlaşmaları ve bunu öğrenmeleri gerekiyor. Bu bağlamda günümüz iş modelinin temel sorunu “hâlâ analitik dünyanın ölü diyagramlarına itibar ediliyor olmasıdır”. Yaşayan çok boyutlu işletmeleri kâğıt üzerindeki iki boyutlu ölü diyagramlara indirgemek faydadan çok zarara yol açmaktadır.
Gelişen enformatik teknolojisinin olanak sağladığı veri depolama kapasitesinin konvansiyonel tekniklerle stratejik bilgiye dönüştürülemediği yaygın olarak paylaşılan bir gerçek. ASO Dergisi 2015 Aralık Sayısında yayınlanan bu çalışma dosyası matematiğin ve istatistiğin ötesinde bu verilerin bilgiye dönüştürülmesi ve risk yönetiminde kullanılması olanaklarını araştırıyor.
Big Data yani büyük veri nedir diyorsanız ve büyük veri analizinin ne gibi yararlar sağlayacağını merak ediyorsanız sizin için Renerald olarak bu sunumu hazırladık. Büyük veri analizleri sayesinde, stratejilerinizi bilimsel veriler ışığında geliştirip şirketinize inanılmaz artı değerler kazandırabileceksiniz.
RECOVERY: Olay sonrası sistemleri düzeltmekAlper Başaran
Siber saldırıların sayısında görülen artış ve saldırganların beceri düzeyinde gözlemlenen iyileşme sonucunda kuruluşların bir siber güvenlik ihlali yaşama ihtimalleri artmaktadır.
Saldırıyı önlemeye odaklanan siber güvenlik yaklaşımının pek çok kuruluş için yetersiz kaldığını gözlemlediğimiz yüzlerce olay yaşandı ve yaşanmaya devam ediyor. Günümüz şartlarında bir kuruluşun siber güvenlik ihlali yaşaması halinde yapacaklarını bilmesi ve olay sonrası durumunu/sistemlerini düzeltmek için izleyeceği bir metodolojiye sahip olması çok önemlidir.
Bu webinarımızda yaşanması muhtemel bir siber güvenlik olayı sonrasında yapılması gerekenleri ve izlenmesi gereken yolu ele alacağız.
Webinarın amacı
Kuruluş bünyesinde, ağır veya hafif etkili, yaşanacak bir güvenlik ihlali sonrasında izlenebilecek bir yol haritası paylaşmak.
Kimler katılmalı
BT Birim çalışanları ve yöneticileri, risk birimi yöneticileri, SOME (Siber Olaylara Müdahale Ekibi) üyeleri
Big Data and Implications on Platform ArchitectureOdinot Stanislas
This document discusses big data and its implications for data center architecture. It provides examples of big data use cases in telecommunications, including analyzing calling patterns and subscriber usage. It also discusses big data analytics for applications like genome sequencing, traffic modeling, and spam filtering on social media feeds. The document outlines necessary characteristics for data platforms to support big data workloads, such as scalable compute, storage, networking and high memory capacity.
The document discusses a unified data architecture that enables any user to access and analyze any data type from data capture through analysis. It describes using a discovery platform to enable interactive data discovery on structured and unstructured data without extensive modeling. It also describes using an integrated data warehouse for cross-functional analysis, shared analytics, and lowest total cost of ownership. Finally, it provides examples of using the architecture for IPTV quality of service analysis, including predictive models using decision trees and naive Bayes.
This document discusses maximizing returns from a data warehouse. It covers the need for real-time data integration to power business intelligence and enable timely, trusted decisions. It outlines challenges with traditional batch-based approaches and how Oracle's data integration solutions address these through products that enable real-time data capture and delivery, bulk data movement, and data quality profiling to build an enterprise data warehouse.
Big Data, Big Content, and Aligning Your Storage StrategyHitachi Vantara
Fred Oh's presentation for SNW Spring, Monday 4/2/12, 1:00–1:45PM
Unstructured data growth is in an explosive state, and has no signs of slowing down. Costs continue to rise along with new regulations mandating longer data retention. Moreover, disparate silos, multivendor storage assets and less than optimal use of existing assets have all contributed to ‘accidental architectures.’ And while they can be key drivers for organizations to explore incremental, innovative solutions to their data challenges, they may provide only short-term gain. Join us for this session as we outline the business benefits of a truly unified, integrated platform to manage all block, file and object data that allows enterprises can make the most out of their storage resources. We explore the benefits of an integrated approach to multiprotocol file sharing, intelligent file tiering, federated search and active archiving; how to simplify and reduce the need for backup without the risk of losing availability; and the economic benefits of an integrated architecture approach that leads to lowering TCSO by 35% or more.
This document discusses big data solutions and analytics. It defines big data in terms of volume, velocity, and variety of data. It contrasts big data analytics with traditional business intelligence, noting that big data looks for untapped insights rather than dashboards. It also provides examples of scalable big data platform architectures and advanced analytics capabilities. Finally, it outlines Anexinet's big data offerings including strategy, starter solutions, projects, and partnerships.
Information Management: Answering Today’s Enterprise ChallengeBob Rhubart
As presented by George Lumpkin at OTN Architect Day, Redwood Shores, CA, 7/22/09.
Find an OTN Architect Day event near you: http://www.oracle.com/technology/architect/archday.html
Interact with Architect Day presenters and participants on Oracle Mix: https://mix.oracle.com/groups/15511
Introducing the Big Data Ecosystem with Caserta Concepts & TalendCaserta
This document summarizes a webinar presented by Talend and Caserta Concepts on the big data ecosystem. The webinar discussed how Talend provides an open source integration platform that scales to handle large data volumes and complex processes. It also overviewed Caserta Concepts' expertise in data management, big data analytics, and industries like financial services. The webinar covered topics like traditional vs big data, Hadoop and NoSQL technologies, and common integration patterns between traditional data warehouses and big data platforms.
Hadoop's Opportunity to Power Next-Generation ArchitecturesDataWorks Summit
(1) Hadoop has the opportunity to power next-generation big data architectures by integrating transactions, interactions, and observations from various sources.
(2) For Hadoop to fully power the big data wave, many communities must work together, including being diligent stewards of the open source core and providing enterprise-ready solutions and services.
(3) Integrating Hadoop with existing IT investments through services, APIs, and partner ecosystems will be vitally important to unlocking the value of big data.
Complex Event Processing (CEP) analyzes streams of event data to identify patterns and derive meaningful information. CEP allows for real-time situational awareness, immediate response, and better decision making using continuous intelligence. The Sybase Event Stream Processor is a CEP engine that can process unlimited input streams at high speeds with low latency. It is used for applications like fraud detection, system automation based on real-time conditions, streaming analytics, and continuous intelligence for better decisions.
The document discusses big data and big analytics. It notes that big data refers to situations where the volume, velocity, and variety of data exceeds an organization's storage and processing capabilities. It then outlines SAS's approach to high-performance analytics, including in-memory architecture, grid computing, and in-database analytics to enable real-time insights from large and diverse datasets. Several case studies demonstrate how SAS solutions have helped customers significantly reduce analytics processing times and improve outcomes.
As the Big Data market has evolved, the focus has shifted from data operations (storage, access and processing of data) to data science (understanding, analyzing and forecasting from data). And as new models are developed, organizations need a process for deploying analytics from research into the production environment. In this talk, we'll describe the five stages of real-time analytics deployment:
Data distillation
Model development
Model validation and deployment
Model refresh
Real-time model scoring
We'll review the technologies supporting each stage, and how Revolution Analytics software works with the entire analytics stack to bring Big Data analytics to real-time production environments.
Apache Hadoop and the Big Data Opportunity in Banking
The document discusses Apache Hadoop and how it can help banks leverage big data opportunities. It provides an overview of what Apache Hadoop is, how it works, and the core projects. It then discusses how Hadoop can help banks create value by detecting fraud, managing risk, improving products based on customer data analysis, and more. The presenters are from Hortonworks, the lead commercial company for Hadoop, and Tresata, a company focused on using Hadoop for banking applications.
Splunk is a big data company founded in 2004 that provides a platform for collecting, indexing, and analyzing machine-generated data. It has over 5,000 customers in over 80 countries across various industries. Splunk's software can handle large volumes of machine data, scaling to terabytes per day and thousands of users. It collects and indexes machine data from various sources like logs, metrics, and applications without needing prior knowledge of schemas or custom connectors.
ScaleBase Webinar 8.16: ScaleUp vs. ScaleOutScaleBase
This document discusses scaling MySQL databases. It outlines the differences between scale up versus scale out approaches. Scale up involves upgrading hardware and optimizing the database, but has limits. Scale out uses replication and sharding to distribute data across multiple database servers to improve performance and allow scaling of reads and writes. The document provides examples of how scale out provides benefits like automatic data distribution, parallel query execution, and flexibility without downtime.
Hadoop's Role in the Big Data Architecture, OW2con'12, ParisOW2
This document discusses big data and Hadoop. It provides an overview of what constitutes big data, how Hadoop works, and how organizations can use Hadoop and its ecosystem to gain insights from large and diverse data sources. Specific use cases discussed include using Hadoop for operational data refining, exploration and visualization of data, and enriching online applications. The document also outlines Hortonworks' strategy of focusing on Apache Hadoop to make it the enterprise big data platform and providing support services around their Hadoop distribution.
In this slidecast, Richard Treadway and Rich Seger from NetApp discuss the company's storage solutions for Big Data and HPC. The company's HPC solutions for Lustre support massive performance and storage density without sacrificing efficiency.
Italya Posta Teskilatı Sybase Afaria KullaniyotSybase Türkiye
Poste Italiane equipped 44,000 mail carriers in Italy with mobile devices using SAP Afaria for mobile device management. This allowed the carriers to provide new services directly to customers and improved operations. SAP Afaria helped Poste Italiane maintain over 25,000 mobile devices with 99% availability.
SAP REAL TIME DATA PLATFORM WITH SYBASE SUPPORTSybase Türkiye
The document describes SAP's real-time data platform, which provides a single architecture for data management and applications. It offers benefits like IT landscape simplification, flexibility to innovate at an organization's own pace, and lower total cost of ownership. The platform includes the SAP HANA in-memory database, Sybase databases, and data management products to transact, store, process and analyze data in real time.
The document provides an overview of the SAP Sybase Event Stream Processor (ESP). Key points include:
- ESP allows for continuous insight and immediate response by analyzing events as they occur in real-time.
- It enables rapid application development through reduced dependence on specialist programming skills and faster implementation/deployment times.
- ESP has a non-intrusive deployment model that can adapt to existing data models and event-driven architectures.
- Key concepts of ESP include input streams, derived streams, windows, and continuous queries to filter, aggregate, and join streaming data.
- ESP Studio provides both visual and textual authoring capabilities using the Continuous Computation Language (CCL).
SAP Sybase IQ uses a technique called distributed query processing (DQP) that can improve query performance by breaking queries into pieces and distributing the pieces across multiple SAP Sybase IQ servers. DQP provides both intra-query and inter-query parallelism. It dynamically manages resources to balance workloads and avoid saturating the system. For DQP to be effective, the storage area network must have sufficient performance to support the increased parallelism.
To develop for the Android platform, developers need the Android SDK, which includes tools for developing, testing, and debugging apps. The primary programming language is Java. Developers create apps by writing code and designing user interfaces in XML layout files. Apps are tested on emulators and devices before being distributed via the Google Play Store.
Mobile device management provides security, visibility, and control for organizations using mobile devices. Without proper management, mobile devices face challenges including limited security, inconsistent connectivity, and a lack of centralized visibility and control for IT. Effective device management is needed to overcome these challenges, protect sensitive data, enforce compliance with regulations, and maximize the benefits of an organization's mobile workforce.
Sybase IQ is an analytic database management system designed for advanced analytics, data warehousing, and business intelligence environments. It can handle massive volumes of structured and unstructured data. Sybase IQ uses a column-oriented approach that stores and retrieves data by column rather than row, providing a 10-100 times performance boost over row-oriented databases. It also uses a shared-everything architecture that allows for massively parallel processing across an elastic computing grid for high scalability.
1. The customer asked the author to build an analytical platform to store data in a database and perform statistical analysis from a front-end interface.
2. The author chose an SAP Sybase IQ column-store database to store data, the open-source R programming language to perform statistical analysis, and RStudio as the front-end interface.
3. The solution provided a simple way to load and query large amounts of data, automated running of statistical models, and could be deployed in the cloud.
The Q2 2012 Appcelerator/IDC Mobile Developer Survey Report found:
1) Apple's iOS opened a 16% lead over Google's Android among developers who said which platform would win in the enterprise, at 53% to 37%.
2) Interest in developing for Android stabilized after declining in previous surveys, with 78% of developers saying they were very interested in Android phones and 69% in tablets.
3) Developers were cautiously optimistic about Windows 8 tablets, seeing opportunity for Microsoft to displace Android as the number two platform, though interest in Windows phones dropped sharply.
This document provides a comprehensive analysis comparing the data modeling capabilities of Sybase PowerDesigner 16.0 InformationArchitect and CA ERwin Data Modeler r8.1 Standard Edition. It examines how each tool supports key data modeling activities like creating different types of data models (conceptual, logical, physical), impact analysis across model levels, and model integration. The analysis finds that while both tools allow creating different model types and linking models, PowerDesigner provides more robust, integrated support through dedicated model types and built-in impact/lineage analysis. It concludes PowerDesigner better enables managing relationships across complex data modeling projects.
This document provides an executive summary of a white paper that reviews SAP Sybase IQ 15.4, a database platform designed to support business analytics and big data workloads. The white paper was sponsored by Sybase Inc. and conducted by independent analyst firm WinterCorp. Key points covered in the executive summary include:
- SAP Sybase IQ 15.4 aims to make the entire analytics process work smoothly and cost-effectively for both structured and unstructured data.
- It features a new analytic services layer, parallel processing with Hadoop, support for the R language, and expanded ecosystem support from third parties.
- At its core is a mature columnar database with data compression and query optimization capabilities designed for
14 Haziran 2012 tarihinde Sybase Türkiye tarafından yapılan PowerDesigner etkinliğinde, PowerDesigner'in SAP içindeki stratejik yol haritası hakkında temel bilgiler
This document discusses the importance of modeling and metadata management for businesses. It notes that 60% of IT projects fail or only partially succeed due to poor alignment between business and IT. Effective modeling helps ensure business goals, rules and requirements are met and that changes can be implemented with minimal risk, time and cost. Metadata management provides business agility, aids regulatory compliance, and forms the foundation for service-oriented architectures. The document promotes PowerDesigner as the leading tool for conceptual, logical and physical data modeling as well as enterprise architecture and metadata management.
This document discusses Replication Server - Real Time Loading (RTL) for replicating data from a source database in real-time to Sybase IQ for analytics purposes. It provides dial-in numbers and passcode for a presentation on the topic. The presentation will cover limitations of pre-RS 15.5 replication solutions to IQ, an overview of RTL, and the new RTL update capabilities in RS.
This document discusses the challenges of managing mobile applications at an enterprise scale. As more employees use mobile devices for work, the number of applications a company needs to support grows exponentially. Managing hundreds or thousands of applications across different devices and operating systems is a significant challenge. The document recommends adopting a common application development platform to simplify management. It also advocates for tools that can remotely distribute, configure, update and remove applications over a device's lifecycle.
Mobile devices are proliferating globally, with over 1 billion smartphones and tablets expected by 2016. This rapid adoption of mobile represents a shift to new systems of engagement that empower customers, partners and employees through context-aware apps and services. For CIOs, developing a formal mobile strategy including designating a chief mobility officer is critical to coordinating investments to build these new systems of engagement across the enterprise through a "design for mobile first" approach.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
4. BIG DATA ANALYTICS ISSUES
DEALING WITH VOLUME, VARIETY, VELOCITY, COSTS, SKILLS
Volume
Managing and
harnessing massive
data sets
Skills Variety
Lack of adequate BIG Harmonizing silos of
skills for popular structured and
APIs
DATA unstructured data
ANALYTICS
Costs Velocity
Too expensive to Keeping up with
acquire, operate, unpredictable data
and expand and query flows
5. BIG DATA ANALYTICS MATURITY
FROM JARGON TO TRANSFORMATIONAL BUSINESS VALUE*
New Strategies &
Business Models
Column Store
Hadoop
Big data
NoSQL In memory
Business
data MPP
Value*
Operational Revenue
Efficiencies Growth
*A McKinsey study titled “Big Data: Next frontier for innovation, competition, and productivity”, May 2011, has found huge potential for Big Data
Analytics with metrics as impressive as 60% improvements in Retail operating margins, 8% reduction in (US) national healthcare expenditures, and $150M
savings in operational efficiencies in European economies
6. BIG DATA ANALYTICS IN THE REAL WORLD
PREVALENT IN DATA INTENSIVE VERTICALS AND FUNCTIONAL AREAS
BIG DATA
Verticals ANALYTICS Functional
Banking • Marketing Analytics
Digital channels
Track visits, discover best channel mix:
Telcom, email, social media, search
• Sales Analytics
Global Capital Markets Deep correlations
Predict risks based on deal DNA (emails,
Retail meetings) pattern match
• Operational Analytics
Government Atomic machine data
Analyze RFIDs, weblogs, SMS, sensors —
continuous operational inefficiency
Healthcare • Financial Analytics
Detailed simulations
Information Providers Liquidity, portfolio simulations —
Stress tests, error margins
8. CAUSAL LINKS: VARIETY, VELOCITY, VOLUME
Events data
Transactional data
µSeconds
Multi-media data
eCommerce data Continuous and/or Bursts Routinely Petabytes
x
w a y
z
Graph data
Variety Velocity Volume
9. GROWING USER COMMUNITIES
Data Scientists Business Analysts Developers/Programmers
Administrators
Business users External consumers Business Processes
10. HARDWARE IS SUPERIOR
Small Server farms – Scale out Larger Servers with partitions – Scale up
Spinning disks to SSDs SSD
1.2x to 2x speed up
SSD SSDs to Main Memory
4x to 200x speed up
Main Memory to CPU caches
CPU Caches
2x to 6x speed up
11. SOFTWARE EXPECTATIONS HAVE CHANGED
Intelligence & Automation
Execution
Characteristics
Performance & Scalability
Results
Characteristics
Traditional Contemporary
16. RESULTS CHARACTERISTICS
ACCURACY TOLERANCE FOCUS
Complex schemas
Multiple applications
Write on schema
Atomic level locking
Consistency guarantees across system losses
Declarative API
Interactive
Does encapsulate elements of CAP
Traditional Associated with SQL
Simple read on schemas
Single application
Batch oriented
Snapshot isolations
Eventual consistency guarantees
Procedural APIs
Does encapsulate elements of ACID
Contemporary Associated with NoSQL
18. COMPREHENSIVE 3-TIER FRAMEWORK
COMMERCIAL AND/OR OPEN SOURCE
Eco-System
Business Intelligence Tools, Data Integration Tools, DBA Tools, Packaged Apps
Application Services
In-Database Analytics, Multi-lingual Client APIs, Federation, Web Enabled
Data Management
High Performance, Highly Scalable, Cloud Enabled
19. RELIABLE DATA MANAGEMENT
Full Mesh High Speed Interconnect
Data
Management
Can handle high performance, compression, batch, ad-hoc analysis
Can routinely scale to Petabyte class problems, thousands of concurrent jobs
Typical characteristics
Massively parallel processing of complex queries
In-memory and on-disk optimizations
Elastic resources for user communities
ACID guarantees
Data variety
Information lifecycle management
User friendly automation tools
File systems (schema free) and/or DBMS structures (schema specific)
20. DATA MANAGEMENT INFRASTRUCTURE
ROBUST, SCALABLE, HIGH PERFORMANCE
Data Discovery Application Modeling Reports/Dashboards Business Decisions
(Data Scientists) (Business Analysts) (BI Programmers) (Business End Users)
Infrastructure
Management Full Mesh High Speed Interconnect
(DBAs)
• Dynamic, elastic MPP grid
– Grow, shrink, provision on-demand
– Heavy parallelization
• Load, prepare, mine, report in a workflow
– Privacy through isolation of resources
– Collaboration through sharing of results/data via sharing of resources
21. VERSATILE APPLICATION SERVICES
Python ADO.NET PERL
Programming PHP Ruby Java C++
APIs
Web Services API
Application Services
In-Database Analytics Plug-Ins: SQL, PMML, C++, JAVA, …
Comprehensive declarative and procedural APIs
In-Database Analytics Plug-In APIs
In-Database Web Services
Query and data federation APIs
Multi-lingual Client APIs
22. VERSATILE APPLICATION SERVICES
RICH ALGORITHMS CLOSE TO DATA
Sybase IQ Process
In Memory
Sybase IQ Process
RPC CALLS
In Memory
User’s DLL “A” User’s DLL “B”
Library Access Process
LOAD
User’s DLL “A” User’s DLL “B”
LOAD
User’s DLL “B”
User’s DLL “B”
In-database + In-process
Multi-lingual APIs
• In-process dynamically loaded In-database + Out-process
Scalar to Scalar
shared libraries
• Out of process shared library
Scalar sets to Aggregate
• Highest possible performance
Scalar sets to Dimensional • Lower security risks
• Incurs security risks, but Aggregates
manageable via privileges • Lower robustness risks
Scalar sets to Multi-attribute
(bulk)
• Incurs robustness risks, but • Lower performance than in-
Multi-attribute (bulk) to
manageable via multiplex process but better than out of
Multi-attribute (bulk)
database
23. VERSATILE APPLICATION SERVICES
NATIVE MAPREDUCE
For stocks in enterprise software sector, find max relative strength of a stock for a trading day*
Key (k1) Value (v1) Key (k2) Value (v2)
Ticker 30-min interval Weighted variance = (A given stock’s variance
30-min Ticker TickValu TickValue
Symbol time / Average Variance across All “N” stocks)
interval time Symbol e Day 1 Day 2
SAP 9:30 am +1.4 / (SUM (+1.4-2.8-0.7….)/”N” stocks)
9:30 am SAP 51 52.4 SAP 10:00 am +2.2 / (SUM (+2.2-2.3-1.1 ….)/”N” stocks)
9:30 am ORCL 31 28.2 Map SAP …… ……
9:30 am TDC 22 21.3 Fn ORCL 9:30 am -2.8 / (SUM (+1.4-2.8-0.7….)/”N” stocks)
ORCL 10:00 am -2.3 / (SUM (+2.2-2.3-1.1 ….)/”N” stocks)
10:00 am SAP 50.9 53.1
ORCL ……. …..
10:00 am TDC 21.8 20.9 TDC 9:30 am -0.7 / (SUM (+1.4-2.8-0.7….)/”N” stocks)
10:00 am ORCL 29.4 27.1 TDC 10:00 am -1.1/ (SUM (+2.2-2.3-1.1 ….)/”N” stocks)
….. ORCL …… ….. TDC ….. ……
Reduce
Fn
Value (v3)
Ticker Symbol Max Absolute Weighted Variance (v3)
SAP Max (ABS(9:30 Wt Var), ABS(10:00 Wt Var), …..)
ORCL Max (ABS(9:30 Wt Var), ABS(10:00 Wt Var), …..)
TDC Max (ABS(9:30 Wt Var), ABS(10:00 Wt Var), …..)
*Calculate max variance for the day by comparing each 30-min interval tick values across two days: the trading day & the
day before, weighted by average variance of all stocks for each 30-min interval
24. VERSATILE APPLICATION SERVICES
NATIVE MAPREDUCE – DECLARATIVE WAY
For stocks in enterprise software sector, find max relative strength of a stock for a trading day
• Map function declaration: CREATE PROCEDURE MapVarTPF (IN XY TABLE (a1 char, a2 datetime, a3 float, a4 float)
RESULT SET YZ (b1 char, b2 datetime, b3 float)
• Reduce function declaration: CREATE PROCEDURE RedMaxVarTPF (IN XY TABLE (a1 char, a2 datetime, a3 float)
RESULTE SET YZ (b1 char, b2 float)
• Query: SELECT RedMaxVarTPF.TickSymb, RedMaxVarTPF.MaxVar,
FROM RedMaxVarTPF (TABLE (SELECT MapVarTPF.TickSymb, MapVarTPF.30MinIntTime, MapVarTPF.Var
FROM MapVarTPF (TABLE ( SELECT TickDataTab.TickSymb, TickDataTab.30MinIntTime,
TickDataTab.30MinValDay1, TickDataTab.30MinValDay2)
OVER (PARTITION BY TickDataTab.30MinInt)))
OVER (PARTITION BY MapVarTPF.TickSymb))
ORDER BY RedMaxVarTPF.TickSymb
• Native MapReduce parallel execution workflow:
MapVarTPF (Partitioned to 15 parallel instances) RedMaxVarTPF (Partitioned to 25 parallel instances) SQL Query collates output using 1 node
……. ……. …..
SAN Fabric SAN Fabric SAN Fabric
• Native MapReduce with unstructured data: Native MapReduce using can easily be applied to unstructured data also e.g.
text, multi-media, … stored in DBMS or to unstructured data brought into DBMS during execution time from external files
25. RICH ECO-SYSTEM
Source Answers
Data preparation Data Usage
Eco-System
DBMS /
Filesystem
Event Processing Data Federation Business Intelligence
Data Modeling / Database Design Tool
Business Intelligence Tools
Data Integration Tools
Data Mining Tools
Application Tools
DBA Tools
26. RICH ECO-SYSTEM
DBMS <–> HADOOP BRIDGE I
Feature Characteristics Big Data Use Cases
• Client tool capable of querying •Ideal for bringing together Big Data
DBMS and Hadoop Analytics pre-computations from
different domains
• Better performance when results
from sources are pre- • Example – In Telecommunication: DBMS
Client Side Federation: Join data computed/pre-aggregated
has aggregated customer loyalty data &
Hadoop with aggregated network
from DBMS AND Hadoop at a client
utilization data; Quest Toad for Cloud can
application level bring data from both sources, linking
customer loyalty to network utilization or
network faults (e.g. dropped calls)
Quest
Toad for Cloud
DBMS Hadoop/Hive
27. RICH ECO-SYSTEM
DBMS <–> HADOOP BRIDGE II
Feature Characteristics Big Data Use Cases
• Extract & load subsets of HDFS
data into DBMS store
• Raw data from HDFS
• Results of Hadoop MR jobs • Ideal for combining subsets of HDFS
ETL unstructured data or summary of
• HDFS Data stored in DBMS is HDFS data into DBMS for mid to long
treated like other DBMS data term usage in business reports
Load Hadoop Data into DBMS • Gets ACID properties of a DBMS
column store: Extract, Transform, • Can be indexed, joined, parallelized • Example – In eCommerce: clickstream data
• Can be queried in an ad-hoc way from weblogs stored in HDFS and outputs of
Load data from HDFS (Hadoop MR jobs on that data (to study browsing
Distributed File System) into DBMS • Visible to BI and other client tools behavior) ETL’d into DBMS. The transactional
schemas sales data in DBMS joined with clickstream data
via DBMS ANSI SQL API only to understand and predict customer browsing
to buying behavior
• Currently, the bulk data transfer
utility SQOOP (built by Cloudera) is
can be used provide this ETL
capability
Clickstream
Data Sales Data
Hadoop/Hive SQOOP DBMS
28. RICH ECO-SYSTEM
DBMS <–> HADOOP BRIDGE III
Feature Characteristics Big Data Use Cases
• Scan and fetch specified data
• Ideal for combining subsets of HDFS
subsets from HDFS via table UDF
data with DBMS data for operational
• Can read and fetch HDFS data subsets
• Called as part of SQL query
(transient) business reports
• Output joinable with DBMS data
Join HDFS data with DBMS data on • Multiple, simultaneous UDF calls possible • Example – In Retail: Point Of Sale (POS)
the fly: Fetch and join subsets of • Sample UDFs provided in JAVA, C++ detailed data stored in HDFS. DBMS EDW
fetches POS data at fixed intervals from HDFS of
HDFS data on-demand using SQL
queries from DBMS(Data • HDFS data not stored in DBMS specific hot selling SKUs, combines with
inventory data in DBMS to predict and prevent
• Fetched into DBMS In-memory tables inventory “stockouts”.
Federation technique) • ACID properties not applicable
• Repeated use: put fetched data in tables
• Visible to BI/other client tools via
ANSI SQL API only
Inventory
POS Data Data
Hadoop/HDFS UDF Bridge DBMS
29. RICH ECO-SYSTEM
DBMS <–> HADOOP BRIDGE IV
Feature Characteristics Big Data Use Cases
• Trigger and fetch Hadoop MR job • Ideal for combining results of
results via table UDF Hadoop MR job results with DBMS
• Can trigger Hadoop MR jobs
• Called as part of Sybase IQ SQL query data for operational (transient)
• Output joinable with Sybase IQ data business reports
• No multiple, simultaneous UDF calls
• Sample UDFs provided in JAVA only
Combine results of Hadoop MR jobs • Example – In Utilities: Smart meter and
with DBMS data on the fly: Initiate smart grid data can be combined for load
and Join results of Hadoop MR jobs • HDFS data not stored in DBMS monitoring and demand forecast. Smart grid
on-demand using SQL queries from • Fetched into DBMS In-memory tables transmission quality data (multi-attribute time
• ACID properties not applicable series data) stored in HDFS can be computed
DBMS data (Query Federation • Repeated use: put fetched data in tables via Hadoop MR jobs triggered from DBMS and
technique) combined with Smart meter data stored in
DBMS to analyze demand and workload.
• Visible to BI and other client tools
via DBMS ANSI SQL API only
Smart Grid Smart Meter
Transmission Data consumption data
Hadoop/HDFS UDF Bridge DBMS
30. RICH ECO-SYSTEM
DBMS <–> PREDICTIVE TOOLS BRIDGE
Express Complex Computations In Industry Standard Predictive Modeling
Markup Language (PMML), Plug In Models Close To data for execution
Database Server
DBMS
SQL
Applications Bridge
Universal
Predictions
Plug-In
PMML
UDFs
PMML
PMML
PMML
(models) PMML Preprocessor
(models)
(models) (convert & validate)
31. RICH ECO-SYSTEM
FUNDAMENTALS OF STREAMS TECHNOLOGY
Process data without storing it
Input Streams
Events arrive on input streams
Derived Streams, Windows
Apply continuous query
operators to one or more
input streams to produce
a new stream
Continuous Queries create a new Windows can Have State
“derived” stream or window • Retention rules define how many or how
long events are kept
SELECT FROM one or more input
• Opcodes in events can indicate
streams/windows
insert/update/delete and can be
WHERE…
automatically applied to the window
GROUP BY…
32. RICH ECO-SYSTEM
STREAMS DATA PROCESSING VS TRADITIONAL DATA PROCESESSING
SQL CCL
Windows on
Tables Event Streams
Rows Events
Columns Fields
On-Demand: query Event-Driven:
runs when information query updates when
is needed information arrives
33. RICH ECO-SYSTEM
STREAMS PRE-PROCESSING
Why store Big Data when you can deal with Small Data – Pre-filter un-necessary data on the fly with Streams technologies
ESP Engine
Alerts Actions
Updates
Memory
Disk
Hadoop/HDFS DBMS
35. 3-LAYER LOGICAL INTEGRATION
STREAM PROCESSING <-> NoSQL <-> DBMS
BI TOOLS DI TOOLS DBA TOOLS DATA MINING TOOLS
Eco-System
Unstructured
Data
App Ingest + Persist (Hadoop,
Services Web 2.0 Java C/C++ SQL Federation
Content Mgmt)
Structured Data
(DBMS)
DMBS
Streaming Data
(ESP)
The heterogeneous world will require co-existence and playing nice!