Today's data economics is flawed. There is a need for a fundamental change in the way we produce, distribute and consume data. This presentation describes a solution with TileDB that can shape the future of data management.
TileDB webinars - Nov 4, 2021
The document summarizes a webinar about TileDB, a universal data management platform that represents data as dense and sparse multi-dimensional arrays. It addresses the data management problems in population genomics by storing variant call data as 3D sparse arrays. TileDB provides a unified storage and serverless computing model that allows efficient data access and analysis at global scale through its open source TileDB Embedded storage and TileDB Cloud platform. The webinar highlights how TileDB solves data production, distribution, and consumption problems and empowers data sharing and collaboration through its marketplace and security features.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
This document provides an overview and summary of TileDB webinars on TileDB Embedded, an embeddable C++ library that stores and accesses multi-dimensional arrays. It discusses who the webinar is for, provides a disclaimer, describes TileDB's origins and investors. The document summarizes what TileDB Embedded is, its performance, open source nature, interoperability, and optimization for cloud. It outlines the webinar agenda covering arrays, internal mechanics, examples, and comparison to other formats.
AIS data management and time series analytics on TileDB Cloud (Webinar, Feb 3...Stavros Papadopoulos
Slides used in the webinar TileDB hosted with participation from Spire Maritime, describing the use and accessibility of massive time series maritime data on TileDB Cloud.
Big data storage systems are designed to store large volumes of immutable data from sources like sensors, social networks, and log files. They provide horizontal and vertical scaling through clustering to ensure size, speed, and availability. Common approaches include NoSQL key-value, document, and column-oriented databases like Redis, MongoDB, and Cassandra that sacrifice transactions for performance but lack standardization and analytics capabilities.
Tatyana Matvienko,Senior Java Developer, Big data storagesAlina Vilk
Big data storage systems address challenges of size, speed, and availability for huge volumes of data from sources like sensors, social networks, and logs. Common approaches include NoSQL distributed databases with horizontal scaling and data replication across clusters. Popular distributed file and key-value storage examples include Amazon, Redis, DynamoDB, and Cassandra which provide high availability through a masterless architecture with no single point of failure and support for rapid horizontal scaling.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
Today's data economics is flawed. There is a need for a fundamental change in the way we produce, distribute and consume data. This presentation describes a solution with TileDB that can shape the future of data management.
TileDB webinars - Nov 4, 2021
The document summarizes a webinar about TileDB, a universal data management platform that represents data as dense and sparse multi-dimensional arrays. It addresses the data management problems in population genomics by storing variant call data as 3D sparse arrays. TileDB provides a unified storage and serverless computing model that allows efficient data access and analysis at global scale through its open source TileDB Embedded storage and TileDB Cloud platform. The webinar highlights how TileDB solves data production, distribution, and consumption problems and empowers data sharing and collaboration through its marketplace and security features.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
This document provides an overview and summary of TileDB webinars on TileDB Embedded, an embeddable C++ library that stores and accesses multi-dimensional arrays. It discusses who the webinar is for, provides a disclaimer, describes TileDB's origins and investors. The document summarizes what TileDB Embedded is, its performance, open source nature, interoperability, and optimization for cloud. It outlines the webinar agenda covering arrays, internal mechanics, examples, and comparison to other formats.
AIS data management and time series analytics on TileDB Cloud (Webinar, Feb 3...Stavros Papadopoulos
Slides used in the webinar TileDB hosted with participation from Spire Maritime, describing the use and accessibility of massive time series maritime data on TileDB Cloud.
Big data storage systems are designed to store large volumes of immutable data from sources like sensors, social networks, and log files. They provide horizontal and vertical scaling through clustering to ensure size, speed, and availability. Common approaches include NoSQL key-value, document, and column-oriented databases like Redis, MongoDB, and Cassandra that sacrifice transactions for performance but lack standardization and analytics capabilities.
Tatyana Matvienko,Senior Java Developer, Big data storagesAlina Vilk
Big data storage systems address challenges of size, speed, and availability for huge volumes of data from sources like sensors, social networks, and logs. Common approaches include NoSQL distributed databases with horizontal scaling and data replication across clusters. Popular distributed file and key-value storage examples include Amazon, Redis, DynamoDB, and Cassandra which provide high availability through a masterless architecture with no single point of failure and support for rapid horizontal scaling.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
The document discusses big data processing systems. It begins with an overview of big data and its evolution due to technologies like IoT, social media, and smart cars. This has led to an exponential increase in data volume and variety, including structured, semi-structured and unstructured data. Traditional databases cannot handle this type and size of data. The document then introduces Hadoop as an open source framework to process large, diverse datasets across clusters. It uses HDFS for storage and MapReduce for parallel processing of data stored in HDFS. Hadoop provides scalable solutions to the problems of storing huge, growing datasets and processing complex, diverse data faster.
This document provides an overview of Cassandra concepts including its distributed architecture, data distribution and replication, tunable consistency, data modeling using schemas and primary keys, and querying data using the Cassandra Query Language (CQL). Key points covered include Cassandra's peer-to-peer node architecture, replication strategies, consistency levels, data structures like tables and columns, primary keys for partitioning and clustering data, and limitations of CQL compared to SQL.
The document discusses the need for a new open source database management system called SciDB to address the challenges of storing and analyzing extremely large scientific datasets. SciDB is being designed to handle petabyte-scale multidimensional array data with native support for features important to science like provenance tracking, uncertainty handling, and integration with statistical tools. An international partnership involving scientists, database experts, and a nonprofit company is developing SciDB with initial funding and use cases coming from astronomy, industry, genomics and other domains.
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
Handling Electronic Health Records Logs with Hadoop - StampedeCon 2015StampedeCon
This document summarizes Mercy's efforts to manage access logs from electronic health records (EHRs) in Hadoop. It discusses storing 37 billion log records from EHR use over 7 years in various database models like Oracle, column stores, Hadoop's HBase and Hive, and Apache Solr. It describes challenges faced with each approach and lessons learned from implementing a Phoenix/HBase solution to index logs by user, patient, and time for analysis. Key takeaways include using Python UDFs in Hive, designing HBase keys to reduce hotspots, ensuring Phoenix uses indexes, and challenges with Kerberos and integrating Phoenix with business intelligence tools.
Big Data is still a challenge for many companies to collect, process, and analyze large amounts of structured and unstructured data. Hadoop provides an open source framework for distributed storage and processing of large datasets across commodity servers to help companies gain insights from big data. While Hadoop is commonly used, Spark is becoming a more popular tool that can run 100 times faster for iterative jobs and integrates with SQL, machine learning, and streaming technologies. Both Hadoop and Spark often rely on the Hadoop Distributed File System for storage and are commonly implemented together in big data projects and platforms from major vendors.
Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...yashbheda
Big data is generated from various sources like users, systems, and devices. It has grown exponentially due to factors like volume, velocity, variety, and veracity. Analyzing big data helps optimize network resources, improve security monitoring, enable targeted marketing, and enhance performance evaluation. Implementing big data solutions requires strategies for data collection, analysis, storage, and visualization to extract useful insights at scale.
This document discusses big data, its key characteristics of volume, velocity, and variety, and how large amounts of diverse data are being generated from various sources like mobile devices, social media, e-commerce, and emails. It explains that big data analytics can provide competitive advantages and better business decisions by examining large datasets. Hadoop and NoSQL databases are approaches for processing and storing large datasets across distributed systems.
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
This document discusses big data tools and management at large scales. It introduces Hadoop, an open-source software framework for distributed storage and processing of large datasets using MapReduce. Hadoop allows parallel processing of data across thousands of nodes and has been adopted by large companies like Yahoo!, Facebook, and Baidu to manage petabytes of data and perform tasks like sorting terabytes of data in hours.
Big data refers to large and complex datasets that are difficult to process using traditional database management tools. It involves 4 V's: volume, velocity, variety, and veracity. MapReduce is a programming model used for processing large datasets in parallel across clusters of computers. It involves Map and Reduce procedures. Hadoop is an open-source software framework that implements MapReduce and provides a distributed file system (HDFS) to support data-intensive applications on large clusters. Data science draws on techniques from many fields to extract meaning from data and create data products.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Big data is characterized by large and complex datasets that are difficult to process using traditional software. These massive volumes of data, known by characteristics like volume, velocity, variety, veracity, value, and volatility, can provide insights to address business problems. Google Cloud Platform offers tools like Cloud Storage, Big Query, PubSub, Dataflow, and Cloud Storage storage classes that can handle big data according to these characteristics and help extract value from large and diverse datasets.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across commodity hardware. The core of Hadoop consists of HDFS for storage and MapReduce for processing data in parallel on multiple nodes. The Hadoop ecosystem includes additional projects that extend the functionality of the core components.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
Analyzing LiDAR and SAR data with Capella Space and TileDB (TileDB webinars, ...Stavros Papadopoulos
Slides by Stavros Papadopoulos (TileDB) and Jason Brown (Capella Space) from the joint TileDB-Capella Space webinar held in April 2022 on SAR and LiDAR data analytics.
The document discusses big data processing systems. It begins with an overview of big data and its evolution due to technologies like IoT, social media, and smart cars. This has led to an exponential increase in data volume and variety, including structured, semi-structured and unstructured data. Traditional databases cannot handle this type and size of data. The document then introduces Hadoop as an open source framework to process large, diverse datasets across clusters. It uses HDFS for storage and MapReduce for parallel processing of data stored in HDFS. Hadoop provides scalable solutions to the problems of storing huge, growing datasets and processing complex, diverse data faster.
This document provides an overview of Cassandra concepts including its distributed architecture, data distribution and replication, tunable consistency, data modeling using schemas and primary keys, and querying data using the Cassandra Query Language (CQL). Key points covered include Cassandra's peer-to-peer node architecture, replication strategies, consistency levels, data structures like tables and columns, primary keys for partitioning and clustering data, and limitations of CQL compared to SQL.
The document discusses the need for a new open source database management system called SciDB to address the challenges of storing and analyzing extremely large scientific datasets. SciDB is being designed to handle petabyte-scale multidimensional array data with native support for features important to science like provenance tracking, uncertainty handling, and integration with statistical tools. An international partnership involving scientists, database experts, and a nonprofit company is developing SciDB with initial funding and use cases coming from astronomy, industry, genomics and other domains.
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
This document discusses how big data is used in Indonesia's pandemic response. It provides an overview of big data and its implementation at the Ministry of Health to manage COVID-19 data. Large volumes of structured and unstructured data from various sources are extracted, transformed, and loaded into Hortonworks Hadoop ecosystem daily. This data is then analyzed with Hive and BigSQL, summarized, and visualized in Tableau dashboards. Lessons learned include the importance of data availability, consistency, and governance to produce insights that help decision making during the pandemic.
Handling Electronic Health Records Logs with Hadoop - StampedeCon 2015StampedeCon
This document summarizes Mercy's efforts to manage access logs from electronic health records (EHRs) in Hadoop. It discusses storing 37 billion log records from EHR use over 7 years in various database models like Oracle, column stores, Hadoop's HBase and Hive, and Apache Solr. It describes challenges faced with each approach and lessons learned from implementing a Phoenix/HBase solution to index logs by user, patient, and time for analysis. Key takeaways include using Python UDFs in Hive, designing HBase keys to reduce hotspots, ensuring Phoenix uses indexes, and challenges with Kerberos and integrating Phoenix with business intelligence tools.
Big Data is still a challenge for many companies to collect, process, and analyze large amounts of structured and unstructured data. Hadoop provides an open source framework for distributed storage and processing of large datasets across commodity servers to help companies gain insights from big data. While Hadoop is commonly used, Spark is becoming a more popular tool that can run 100 times faster for iterative jobs and integrates with SQL, machine learning, and streaming technologies. Both Hadoop and Spark often rely on the Hadoop Distributed File System for storage and are commonly implemented together in big data projects and platforms from major vendors.
Big data (4Vs,history,concept,algorithm) analysis and applications #bigdata #...yashbheda
Big data is generated from various sources like users, systems, and devices. It has grown exponentially due to factors like volume, velocity, variety, and veracity. Analyzing big data helps optimize network resources, improve security monitoring, enable targeted marketing, and enhance performance evaluation. Implementing big data solutions requires strategies for data collection, analysis, storage, and visualization to extract useful insights at scale.
This document discusses big data, its key characteristics of volume, velocity, and variety, and how large amounts of diverse data are being generated from various sources like mobile devices, social media, e-commerce, and emails. It explains that big data analytics can provide competitive advantages and better business decisions by examining large datasets. Hadoop and NoSQL databases are approaches for processing and storing large datasets across distributed systems.
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
This document discusses big data tools and management at large scales. It introduces Hadoop, an open-source software framework for distributed storage and processing of large datasets using MapReduce. Hadoop allows parallel processing of data across thousands of nodes and has been adopted by large companies like Yahoo!, Facebook, and Baidu to manage petabytes of data and perform tasks like sorting terabytes of data in hours.
Big data refers to large and complex datasets that are difficult to process using traditional database management tools. It involves 4 V's: volume, velocity, variety, and veracity. MapReduce is a programming model used for processing large datasets in parallel across clusters of computers. It involves Map and Reduce procedures. Hadoop is an open-source software framework that implements MapReduce and provides a distributed file system (HDFS) to support data-intensive applications on large clusters. Data science draws on techniques from many fields to extract meaning from data and create data products.
An overview about several technologies which contribute to the landscape of Big Data.
An intro about the technology challenges of Big Data, follow by key open-source components which help out in dealing with various big data aspects such as OLAP, Real-Time Online
Analytics, Machine Learning on Map-Reduce. I conclude with an enumeration of the key areas where those technologies are most likely unleashing new opportunity for various businesses.
Big data is characterized by large and complex datasets that are difficult to process using traditional software. These massive volumes of data, known by characteristics like volume, velocity, variety, veracity, value, and volatility, can provide insights to address business problems. Google Cloud Platform offers tools like Cloud Storage, Big Query, PubSub, Dataflow, and Cloud Storage storage classes that can handle big data according to these characteristics and help extract value from large and diverse datasets.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across commodity hardware. The core of Hadoop consists of HDFS for storage and MapReduce for processing data in parallel on multiple nodes. The Hadoop ecosystem includes additional projects that extend the functionality of the core components.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
Analyzing LiDAR and SAR data with Capella Space and TileDB (TileDB webinars, ...Stavros Papadopoulos
Slides by Stavros Papadopoulos (TileDB) and Jason Brown (Capella Space) from the joint TileDB-Capella Space webinar held in April 2022 on SAR and LiDAR data analytics.
Cloudera, Azure and Big Data at Cloudera Meetup '17Nathan Bijnens
The document discusses Microsoft's Azure cloud platform and how it provides a suite of AI, machine learning, and data analytics services to help organizations collect and analyze data to gain insights and make decisions. It highlights several Azure services like Data Lake, Event Hubs, Stream Analytics, and Cognitive Services that allow customers to store and process vast amounts of data and build intelligent applications. Examples are also given of companies using Azure services to modernize their data infrastructure and build predictive models.
Building IoT and Big Data Solutions on AzureIdo Flatow
This document discusses building IoT and big data solutions on Microsoft Azure. It provides an overview of common data types and challenges in integrating diverse data sources. It then describes several Azure services that can be used to ingest, process, analyze and visualize IoT and other large, diverse datasets. These services include IoT Hub, Event Hubs, Stream Analytics, HDInsight, Data Factory, DocumentDB and others. Examples and demos are provided for how to use these services to build end-to-end IoT and big data solutions on Azure.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Modern DW Architecture
- The document discusses modern data warehouse architectures using Azure cloud services like Azure Data Lake, Azure Databricks, and Azure Synapse. It covers storage options like ADLS Gen 1 and Gen 2 and data processing tools like Databricks and Synapse. It highlights how to optimize architectures for cost and performance using features like auto-scaling, shutdown, and lifecycle management policies. Finally, it provides a demo of a sample end-to-end data pipeline.
This document provides an overview of big data and how Azure HDInsight can be used to work with big data. It discusses the evolution of data from gigabytes to exabytes and the big data utility gap where most data is stored but not analyzed. It then discusses how to store everything, analyze anything, and build the right thing using big data. Examples are provided of companies generating large amounts of data. An overview of the Hadoop ecosystem is given along with examples of using Hive and Pig on HDInsight to query and analyze large datasets. A case study of Klout is also summarized.
Dr. Christian Kurze from Denodo, "Data Virtualization: Fulfilling the Promise...Dataconomy Media
This document discusses data virtualization and how it can help organizations leverage data lakes to access all their data from disparate sources through a single interface. It addresses how data virtualization can help avoid data swamps, prevent physical data lakes from becoming silos, and support use cases like IoT, operational data stores, and offloading. The document outlines the benefits of a logical data lake created through data virtualization and provides examples of common use cases.
Solving the Really Big Tech Problems with IoTEric Kavanagh
The Briefing Room with Dr. Robin Bloor and HPE Security
The Internet of Things brings new technological problems: sensor communications are bi-directional, the scale of data generation points has no precedent and, in this new world, security, privacy and data protection need to go out to the edge. Likely, most of that data lands in Hadoop and Big Data platforms. With the need for rapid analytics never greater, companies try to seize opportunities in tighter time windows. Yet, cyber-threats are at an all-time high, targeting the most valuable of assets—the data.
Register for this episode of The Briefing Room to hear Analyst Dr. Robin Bloor explain the implications of today's divergent data forces. He’ll be briefed by Reiner Kappenberger of HPE, who will discuss how a recent innovation -- NiFi -- is revolutionizing the big data ecosystem. He’ll explain how this technology dramatically simplifies data flow design, enabling a new era of business-driven analysis, while also protecting sensitive data.
Atos is promoting its Data Services Solutions (DSS) and data lake evolutions to help customers with their data transformation journeys. The key offerings include:
1) A governed cloud-based data lakehouse for data warehousing/BI modernization and managed DSS services.
2) The Atos Cognitive Data Framework, an open-source, modular data platform that can deploy on public, private or hybrid clouds and interconnect with tools from major vendors.
3) Capabilities for data management, analysis, monetization and more through a "sovereign, modular and open" data platform.
These slides gives an overview of NoSQL in the context of Big Data processing. We start by defining SQL vs NoSQL concepts, the CAP theorem, and why NoSQL technologies are needed. Then we discuss the various NoSQL technology breeds, including Key/Value stores, Document stores, Column Family (wide-column) stores, memory cache stores, and graph stores, along with related tools and examples. After that we present various solution architecture patterns, in which NoSQL data stores play viable roles. Next we delve into Microsoft Azure implementation of some of these NoSQL technologies, including Redis Cache, Azure Table Storage, HBase on HDInsight, and Azure DocumentDB. Finally, we conclude with some useful resource, before we give a sneak peek on how to use neo4j for Graph Processing.
Facebook's data warehouse processes petabytes of data daily to support data-driven development, business decisions, and machine learning. Hive provides a SQL interface and metadata management on top of Hadoop to simplify querying large datasets. At Facebook, Hive is used extensively for reporting, ad hoc analysis, and assembling machine learning training data. The Hive cluster processes 800TB of data and 10,000-25,000 jobs daily. Future work includes improving performance, scaling to support dynamic workloads and data growth, enabling incremental loads, and full SQL support.
Databases are used to store and organize data for fast retrieval. They have several objectives like speedy retrieval, ordering, and conditional grouping of data. Database management systems (DBMS) help manage databases by defining entities, storage architecture, security, backups and more. Relational database management systems (RDBMS) are most common today and follow Codd's rules. Databases can be classified by usage (operational, data warehouse, analytical), processing type (single, distributed), storage type (flat file, indexed, trees), and content scope (legacy, hypermedia). Database contents include tables with rows and columns to store entity attributes and records. Tables have field/column definitions specifying name, data type, size and other properties
How to Radically Simplify Your Business Data ManagementClusterpoint
Relational databases were designed for tabular data storage model. It requires complex software: schemas, encoded data, inflexible relations, sophisticated indexes. Complexity of your IT systems increases over your database life-time many-fold. Your costs too. Yet, we have a solution for this.
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者:Informatica 資深產品顧問 | 尹寒柏
議題簡介:Big Data 時代,比的不是數據數量,而是了解數據的深度。現在,因為 Big Data 技術的成熟,讓非資訊背景的 CXO 們,可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞,從 BI 進入 CI,更連結消費者經濟的脈動,洞悉顧客的意圖。不過,有個 Big Data 時代要 注意的思維,那就是競爭到最後,不單只是看數據量的增長,還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度,Informatica 也有能力提供更快速彙集數據技術,從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案,是精誠集團在 Big Data 時代的最佳工具。
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Perchè un programmatore ama anche i database NoSQLMarco Parenzan
Per quale motivo i programmatori parlano tanto di NoSql? Non amano più Sql Server e il linguaggio Sql in generale? No. La complessità delle applicazioni Web e Cloud necessitano di soluzioni complesse, che soddisfano potenzialità e vincoli imposti dal mondo web. Oggi infatti si parla di Polyglot Persistence, di CQRS e altro. Obiettivo di questa sessione è far comprendere i nuovi principi cui aderiscono i web developers e abbassare l' "impedance mismatch" che sembra essersi creato con i dba e e db devs.
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
2. Disclaimer
I am the exclusive recipient of complaints
Email me at: stavros@tiledb.com
All the credit for our amazing work goes to our powerful team
Check it out at https://tiledb.com/about
3. Deep roots at the intersection of HPC, databases and data science
Traction with telecoms, pharmas, hospitals and other scientific organizations
40 members with expertise across all applications and domains
Who we are
TileDB got spun out from MIT and Intel Labs in 2017
WHERE IT ALL STARTED
Raised over $20M, we are very well capitalized
INVESTORS
5. Visit tiledb.com for a lot of resources
What you need to know
TileDB is a universal database
All data types (tables, images, video, genomics, LiDAR, etc)
Based on multi-dimensional arrays
TileDB offerings
TileDB Embedded (open-source storage engine)
TileDB Cloud (SaaS / on-prem database)
Numerous APIs and integrations
Numerous backends and cloud-optimized
6. TileDB Cloud
❏ Access control and logging
❏ Serverless SQL, UDFs, task graphs
❏ Jupyter notebooks and dashboards
Unified data management
and easy serverless compute
at global scale
The TileDB Universal Database
Pluggable Compute: Efficient APIs & Tool Integrations
TileDB Embedded
Open-source interoperable
storage with a universal
open-spec array format
❏ Parallel IO, rapid reads & writes
❏ Columnar, cloud-optimized
❏ Data versioning & time traveling
7. What is TileDB Embedded?
An embeddable C library that stores and accesses multi-dimensional arrays
Dense array Sparse array
It implements very fast array slicing across dimensions
8. Superior
performance
Built in C
Fully-parallelized
Columnar format
Multiple compressors
R-trees for sparse arrays
TileDB Embedded at a Glance
https://github.com/TileDBInc/TileDB
Open source:
Rapid updates
& data versioning
Immutable writes
Lock-free
Parallel reader / writer model
Time traveling
Schema evolution
9. TileDB Embedded at a Glance
https://github.com/TileDBInc/TileDB
Open source:
Extreme
interoperability
Numerous APIs
Numerous integrations
All backends
Optimized
for the cloud
Immutable writes
Parallel IO
Minimization of requests
11. Unified Data Management
Everything in TileDB Cloud is an array
All data, notebooks, UDFs, dashboards, ML models
A single platform for data management
Catalogs, descriptions, metadata and exploration
Access control
Logging
A single UI, everything accessible via REST
12. Notebooks
Embedded JupyterHub instances in the TileDB Cloud UI
Notebook management (similar to arrays)
Catalogs, descriptions, metadata and exploration
Access control
Logging
Super easy onboarding and testing
Launch different types
13. Sharing & Logging
Share your work, learn from others, promote science
A massive catalog of analysis-ready datasets
A massive catalog of runnable code
Collaboration and reproducibility
Organizations
Serverless, global-scale infrastructure
14. Serverless Scalable Compute
Serverless slicing and SQL
Serverless UDFs and task graphs
Geo-aware compute dispatch
Zero-infra data and code sharing
Automation, scalability, cost savings
15. Machine Learning
Store and version all ML models along with your data
Catalog, descriptions, metadata, versions, etc.
Sharing and logging
Scalable training and servicing of the models
ML is a data management problem
16. Dashboards
Diversify your visualization options
Create any dashboard via Python widgets, R shiny or other
Dashboards are notebooks, and notebooks are arrays
Launch a dashboard like a notebook in the TileDB UI
Share it, log it, monetize it
17. Monetization
A game-changer for marketplaces
A full marketplace, integrating with Stripe
Monetize everything (data and code)
Zero-infra requirement from the data/code vendor
No more wrangling data and deploying code
19. TileDB Cloud Value Proposition
A single solution for data storage and analysis
Unified data management
Security (authentication, access control, logging)
Better performance at a lower cost
Faster storage and access because of the array engine
Serverless, pay-as-you-go, geo-aware compute
Versatile, scalable compute
Zero-infra data/code sharing and monetization
Create and share any dataset
Unlimited creativity and collaboration
Build and share any code, notebook, ML model or dashboard