This document discusses TIBCO's streaming analytics products and services. It provides an overview of TIBCO Streaming Analytics, BusinessEvents, and StreamBase, highlighting their developer and business user features. It also discusses TIBCO Live Datamart and various accelerators and integrations with predictive analytics and other TIBCO products. The document is confidential and its contents are subject to change.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
To be able to use analytics effectively and thus leverage the data treasures in the company, you need a modern and scalable data platform that can react flexibly to events and was designed with a DataOps mindset from the very beginning.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Introduction SQL Analytics on Lakehouse ArchitectureDatabricks
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
To be able to use analytics effectively and thus leverage the data treasures in the company, you need a modern and scalable data platform that can react flexibly to events and was designed with a DataOps mindset from the very beginning.
Data lineage and observability with Marquez - subsurface 2020Julien Le Dem
This document discusses Marquez, an open source metadata management system. It provides an overview of Marquez and how it can be used to track metadata in data pipelines. Specifically:
- Marquez collects and stores metadata about data sources, datasets, jobs, and runs to provide data lineage and observability.
- It has a modular framework to support data governance, data lineage, and data discovery. Metadata can be collected via REST APIs or language SDKs.
- Marquez integrates with Apache Airflow to collect task-level metadata, dependencies between DAGs, and link tasks to code versions. This enables understanding of operational dependencies and troubleshooting.
- The Marquez community aims to build an open
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Introduction SQL Analytics on Lakehouse ArchitectureDatabricks
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
Data and AI summit: data pipelines observability with open lineageJulien Le Dem
Presentation of Data lineage an Observability with OpenLineage at the "Data and AI summit" (formerly Spark summit). With a focus on the Apache Spark integration for OpenLineage
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
Data Quality Patterns in the Cloud with Azure Data FactoryMark Kromer
This document discusses data quality patterns when using Azure Data Factory (ADF). It presents two modern data warehouse patterns that use ADF for orchestration: one using traditional ADF activities and another leveraging ADF mapping data flows. It also provides links to additional resources on ADF data flows, data quality patterns, expressions, performance, and connectors.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
The document discusses data architecture solutions for solving real-time, high-volume data problems with low latency response times. It recommends a data platform capable of capturing, ingesting, streaming, and optionally storing data for batch analytics. The solution should provide fast data ingestion, real-time analytics, fast action, and quick time to value. Multiple data sources like logs, social media, and internal systems would be ingested using Apache Flume and Kafka and analyzed with Spark/Storm streaming. The processed data would be stored in HDFS, Cassandra, S3, or Hive. Kafka, Spark, and Cassandra are identified as key technologies for real-time data pipelines, stream analytics, and high availability persistent storage.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Oracle Data Integrator (ODI) is an ETL tool acquired by Oracle in 2006. It provides a graphical interface to build, manage, and maintain data integration processes. ODI can extract, transform, and load data between heterogeneous data sources to support business intelligence, data warehousing, data migrations, and master data management projects. It uses a 4-tier architecture with repositories to store metadata and designs, an ODI Studio for development, runtime agents to execute tasks, and a console for monitoring.
How we built this: Data tiering, snapshots, and asynchronous searchElasticsearch
What goes into a major roadmap investment at Elastic? Take a closer look at the technical capabilities that come together to help us deliver on our data tier vision — from asynchronous search to searchable S3/blob store/Google storage, new cold and frozen storage tiers, and more.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
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.
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.
This document discusses challenges with centralized data architectures and proposes a data mesh approach. It outlines 4 challenges: 1) centralized teams fail to scale sources and consumers, 2) point-to-point data sharing is difficult to decouple, 3) bridging operational and analytical systems is complex, and 4) legacy data stacks rely on outdated paradigms. The document then proposes a data mesh architecture with domain data as products and an operational data platform to address these challenges by decentralizing control and improving data sharing, discovery, and governance.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Feature Store as a Data Foundation for Machine LearningProvectus
This document discusses feature stores and their role in modern machine learning infrastructure. It begins with an introduction and agenda. It then covers challenges with modern data platforms and emerging architectural shifts towards things like data meshes and feature stores. The remainder discusses what a feature store is, reference architectures, and recommendations for adopting feature stores including leveraging existing AWS services for storage, catalog, query, and more.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Reusing and Managing R models in an EnterpriseLou Bajuk
1) The document discusses deploying, managing, and reusing R models in an enterprise environment to make data science more accessible.
2) It describes how TIBCO products like Spotfire, Statistica, and Streambase can be used to deploy R models, embed them in applications and visualizations, and score models in real-time.
3) The goal is to allow both data scientists and general users to leverage R models through these tools to drive insights, automate processes, and take real-time actions.
Data and AI summit: data pipelines observability with open lineageJulien Le Dem
Presentation of Data lineage an Observability with OpenLineage at the "Data and AI summit" (formerly Spark summit). With a focus on the Apache Spark integration for OpenLineage
ETL tools extract data from various sources, transform it for reporting and analysis, cleanse errors, and load it into a data warehouse. They save time and money compared to manual coding by automating this process. Popular open-source ETL tools include Pentaho Kettle and Talend, while Informatica is a leading commercial tool. A comparison found that Pentaho Kettle uses a graphical interface and standalone engine, has a large user community, and includes data quality features, while Talend generates code to run ETL jobs.
Data Quality Patterns in the Cloud with Azure Data FactoryMark Kromer
This document discusses data quality patterns when using Azure Data Factory (ADF). It presents two modern data warehouse patterns that use ADF for orchestration: one using traditional ADF activities and another leveraging ADF mapping data flows. It also provides links to additional resources on ADF data flows, data quality patterns, expressions, performance, and connectors.
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
The document discusses data architecture solutions for solving real-time, high-volume data problems with low latency response times. It recommends a data platform capable of capturing, ingesting, streaming, and optionally storing data for batch analytics. The solution should provide fast data ingestion, real-time analytics, fast action, and quick time to value. Multiple data sources like logs, social media, and internal systems would be ingested using Apache Flume and Kafka and analyzed with Spark/Storm streaming. The processed data would be stored in HDFS, Cassandra, S3, or Hive. Kafka, Spark, and Cassandra are identified as key technologies for real-time data pipelines, stream analytics, and high availability persistent storage.
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
MDM, data quality, data architecture, and more. At the same time, combining these foundational data management approaches with other innovative techniques can help drive organizational change as well as technological transformation. This webinar will provide practical steps for creating a data foundation for effective digital transformation.
Oracle Data Integrator (ODI) is an ETL tool acquired by Oracle in 2006. It provides a graphical interface to build, manage, and maintain data integration processes. ODI can extract, transform, and load data between heterogeneous data sources to support business intelligence, data warehousing, data migrations, and master data management projects. It uses a 4-tier architecture with repositories to store metadata and designs, an ODI Studio for development, runtime agents to execute tasks, and a console for monitoring.
How we built this: Data tiering, snapshots, and asynchronous searchElasticsearch
What goes into a major roadmap investment at Elastic? Take a closer look at the technical capabilities that come together to help us deliver on our data tier vision — from asynchronous search to searchable S3/blob store/Google storage, new cold and frozen storage tiers, and more.
Is the traditional data warehouse dead?James Serra
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
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.
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.
This document discusses challenges with centralized data architectures and proposes a data mesh approach. It outlines 4 challenges: 1) centralized teams fail to scale sources and consumers, 2) point-to-point data sharing is difficult to decouple, 3) bridging operational and analytical systems is complex, and 4) legacy data stacks rely on outdated paradigms. The document then proposes a data mesh architecture with domain data as products and an operational data platform to address these challenges by decentralizing control and improving data sharing, discovery, and governance.
Databricks + Snowflake: Catalyzing Data and AI InitiativesDatabricks
"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo.
Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow).
Key Takeaways:
How Databricks & Snowflake work;
Why they're so powerful;
How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Feature Store as a Data Foundation for Machine LearningProvectus
This document discusses feature stores and their role in modern machine learning infrastructure. It begins with an introduction and agenda. It then covers challenges with modern data platforms and emerging architectural shifts towards things like data meshes and feature stores. The remainder discusses what a feature store is, reference architectures, and recommendations for adopting feature stores including leveraging existing AWS services for storage, catalog, query, and more.
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Reusing and Managing R models in an EnterpriseLou Bajuk
1) The document discusses deploying, managing, and reusing R models in an enterprise environment to make data science more accessible.
2) It describes how TIBCO products like Spotfire, Statistica, and Streambase can be used to deploy R models, embed them in applications and visualizations, and score models in real-time.
3) The goal is to allow both data scientists and general users to leverage R models through these tools to drive insights, automate processes, and take real-time actions.
Making Data Science accessible to a wider audienceLou Bajuk
TIBCO's Lou Bajuk talks about the challenges to making Data Science accessible to a wider audience, and how the TIBCO Analytics platform helps our customers tackle those challenges.
Embracing data science for smarter analytics appsLou Bajuk
This document discusses TIBCO's plans to embrace data science and make advanced analytics more accessible. It highlights TIBCO Enterprise Runtime for R (TERR), which allows embedding predictive analytics and the R programming language into TIBCO products like Spotfire for easier use by analysts, engineers and business users. TIBCO aims to help more users leverage data science through guided applications, visual tools, and integration of TERR and other analytics tools into a unified ecosystem.
SL Corporation was pleased to be both a Platinum Sponsor and speaker at TIBCO’S annual user conference, TUCON 2013. TUCON brought together thousands of the world's most influential technologists and decision-makers that form the future of industry. RTView for TIBCO, a lightweight yet comprehensive performance monitoring platform that is tightly integrated with TIBCO middleware was shown at the booth. The solution dramatically increases visibility into TIBCO infrastructure.
TIBCO expert and TIBCO Architect for Asurion, Craig Shelley, and SL’s CTO, Tom Lubinski, presented the following session:
Asurion and SL Present | Tell Me When My Critical Apps Are Sick, Not Dead!
Craig and Tom offered an in-depth look at the latest advances in end-to-end application monitoring and control of TIBCO-centric environments. Learn how you can achieve a significant positive business impact through increased productivity, proactive monitoring, and the agility of being able to quickly change what you’re monitoring and how you view it.
Set Your Course for Change with Real-Time Analytics and InsightsTIBCO Software Inc.
The days where analytics could be an afterthought are over. In this era of unprecedented business change, one needs contextual, real-time insights and the ability to immediately act on them. In these slides, we will take an in-depth look at the power of combining real-time analytics and BPM, the different audiences, the difference between reporting and business intelligence (BI), and how they all come together to bring big benefits to business users and enable change.
Give'em What They Want! Self-Service Monitoring in a Shared Services EnvironmentDavid Hickman
The document discusses implementing self-service monitoring in a shared services environment. It describes how providing developers, support teams, and operations staff with access to monitoring dashboards and historical performance data reduces emergency calls to operations and frees up their time. Specific benefits are outlined for application developers, support teams, and the middleware platform team in quickly identifying issues and making scaling decisions.
Tibco NOW San Diego 2017 RTView Breakout sessionSL Corporation
What’s New With TIBCO Middleware Monitoring was a session given at TIBCO Now San Diego 2017 By Ted Wilson, SL COO and Rahul Kamdar from TIBCO. In this session, they discussed the proactive performance monitoring of TIBCO integration platforms is the most effective way to avoid problems. Learn about how TIBCO® RTView® is constantly evolving to meet demand for consolidated visibility across the latest TIBCO technologies deployed in modern multi-cloud, PaaS, and hybrid environments.
This document discusses TIBCO's OEM partnerships and provides an overview of TIBCO's analytics and data integration products. It highlights challenges faced by various industries and how TIBCO technologies like Spotfire and Insight Platform can help address those challenges. Customer case studies are presented on how Equifax and eClinicalWorks have benefited from using TIBCO Spotfire to gain insights from their data. The document also introduces TIBCO Accelerators which provide pre-built solutions to common use cases to help customers develop applications more quickly.
GeoAnalytics: Maximize the Value of Location Based DataNicola Sandoli
We have quickly come to rely on digital maps for everyday living. Map applications are now gaining traction in the enterprise, with users expecting the same simple and intuitive experiences as in their personal apps.
GeoAnalytics can help organize resources to reduce costs, visualize opportunities, and provide a more accurate way to manage the flow of goods.
PART 1: Intro To JasperReports IO And How To Build Your First ReportTIBCO Jaspersoft
The document provides an agenda for introducing JasperReports IO, which is a data visualization and reporting service that allows for interactive data visualizations using a JavaScript API and report production via REST services. The agenda includes introductions, an overview of what JasperReports IO is and why it was created, a demonstration of it in use, building a first visualization, and polling questions. Key points about JasperReports IO are that it is based on the JasperReports platform and allows for embedded interactive visualizations in web applications and report generation via a REST API.
Information processing and analytics cannot be focused only on “store-first” or batch-based approaches. To provide maximum business value, information must also be analyzed closer to the source, and at the speed in which it is being created. Streaming analytics utilizes various techniques for intelligently processing data as it arrives at the edge or within the data center, with the purpose of proactively identifying threats or opportunities for your business.
INTRODUCING JASPERSOFT ADVANCED DATA SERVICES: DATA VIRTUALIZATION AT SCALETIBCO Jaspersoft
TIBCO has a new, best-in-class data virtualization tool, TIBCO Jaspersoft® Advanced Data Services. This new service can have a big impact on performance—particularly in scenarios that involve combining three or more data sources or accessing high volumes of data. It is also capable of performing complex joins and data transformations that aren’t possible in Jaspersoft® Domains.
Join our customer-exclusive webinar for an introduction to Jaspersoft Advanced Data Services, its use cases, and how it compares to existing Jaspersoft data integration options.
Specifically, you’ll learn:
How Jaspersoft Advanced Data Services compares to other Jaspersoft data integration options: Jaspersoft ETL and Jaspersoft Virtual Data Sources (Domains)
What use case scenarios are particularly well-suited for Jaspersoft Advanced Data Services
Through product demonstrations, how Jaspersoft Advanced Data Services works
How to try Jaspersoft Advanced Data Services for free
Companies must find a way to join both paths and view the transition to digital as a unified journey, with the end goal clearly defined, then utilize APIs to help them get there faster. The question then becomes, how can companies and developers leverage ESBs, APIs, and a Fast Data platform to cultivate innovation?
In my session at 19th Cloud Expo (Nov 2016), I explored this topic further, highlighting specific use cases and the true value that can be gained from the cloud and APIs in this quest
LiveBudget LivaClick by Fincore-EN- PPT-2022Mustafa Kugu
The document describes the LivaBudget budgeting solution and LivaClick business intelligence solution. LivaBudget allows companies to create budgets across all departments, with integrated modules for production, purchasing, HR, sales, expenses, projects, and finance. It facilitates strategic decision making. LivaClick builds on LivaBudget data to provide analytics, reporting, visualization and predictive capabilities to support business insights. Sample dashboards illustrate retrospective and predictive analytics for various business functions like sales, projects, and expenses.
Big Data LDN 2017: How Big Data Insights Become Easily Accessible With Workfl...Matt Stubbs
This document provides an overview of how workflows can help make big data insights more accessible. It discusses how workflows allow customers to benefit from cost reductions and faster deployment times. Examples are given of customers in healthcare and banking that have reduced surgical infection rates and cut model development time in half using workflows. The document also covers how to pull insights together and deploy predictive models to external systems using tools like Tibco Statistica. It provides a technical overview of building predictive analytics workflows for big data, including examples of workflow templates for Spark, H2O, and deep learning with CNTK.
Democratizing Analytics and Data Science for Continuous IntelligenceBipin Singh
TIBCO provides analytics and data science solutions to help Mercedes F1 team optimize car performance. Their solutions help Mercedes analyze vast amounts of test, simulation and real-time racing data to find optimal car setups and strategies. This has helped Mercedes win several recent F1 driver's and constructor's championships. TIBCO solutions also help other companies like Hemlock Semiconductor optimize manufacturing processes and Hunt Oil better monitor oil drilling operations.
Analyze billions of records on Salesforce App Cloud with BigObjectSalesforce Developers
Salesforce hosts billions of customer records on Salesforce App Cloud. Making timely decisions on this invaluable data demands a new set of capabilities. From interacting with data in real-time to leveraging a fluid integration with Salesforce Analytics, these capabilities are just around the corner. Join us in this roadmap session to see what the near-future of Big Data on Salesforce App Cloud looks like and how you can benefit from it.
Key Takeaways
- Learn what 100 billion+ records on the Salesforce App Cloud could actually mean to you.
- Understand new services such as AsyncSOQL that can can deliver reliable, resilient query capabilities over your sObjects and BigObjects.
-Gain insights for large scale federated data filtering and aggregation.
-Transform data movement so all your customer records are available across their life cycle.
Intended Audience
This session is for Salesforce Administrators, Developers, Architects and just about anyone who wants to learn more about BigObjects!
Data Preparation vs. Inline Data Wrangling in Data Science and Machine LearningKai Wähner
Comparison of Data Preparation vs. Data Wrangling Programming Languages, Frameworks and Tools in Machine Learning / Deep Learning Projects.
A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.
This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models.
Key takeaways for the audience:
- Learn various options for preparing data sets to build analytic models
- Understand the pros and cons and the targeted persona for each option
- See different technologies and open source frameworks for data preparation
- Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation
Video Recording / Screencast of this Slide Deck: https://youtu.be/2MR5UynQocs
Similar to Tibco streaming analytics overview and roadmap (20)
R Consortium update for EARL Boston Oct 2017Lou Bajuk
The document provides an overview of the R Consortium, a non-profit organization that supports the R community. It discusses the goals of promoting R's development, funding projects, and fostering collaboration between companies. It outlines the consortium's governance structure and membership levels. Recent projects funded include improving package building, localization, code coverage tools, and database interfaces. TIBCO's participation is driven by contributing to R's success and the compatibility of its products that integrate R.
R consortium update EARL London Sept 2017Lou Bajuk
The document provides an overview of the R Consortium, a non-profit organization that supports the R community. It discusses the goals of promoting R's growth, funding projects, and fostering collaboration between companies. It outlines the consortium's structure, membership options, recently funded projects including improving package building and localization, and encourages involvement through advocacy, proposals, or volunteering.
The document discusses the R Consortium, a non-profit organization that supports the R community. It was founded in 2015 to promote R's growth as a leading data science platform. The R Consortium funds projects and working groups through an Infrastructure Steering Committee to support R and foster collaboration. It is housed at the Linux Foundation to ensure its long-term support of the R community. Membership provides opportunities to influence projects and have a voice in the R community.
The R Consortium is a non-profit organization that supports the R community. Its goals are to create infrastructure and standards to benefit all R users, promote best practices, and support growth and adoption of R. The board of directors and infrastructure steering committee work on collaborative projects. Current projects include improving package development, database interfaces, localization, teaching, and geospatial analysis. Working groups explore new technologies to benefit the R ecosystem.
R in BI and Streaming Applications for useR 2016Lou Bajuk
This document discusses the challenges of applying R in streaming and business intelligence applications and introduces TIBCO's Enterprise Runtime for R (TERR) as a solution. TERR is an enterprise-grade R engine that allows users to develop code in open source R and deploy it on a commercially supported platform without rewriting code. This enables easy integration of R into applications for real-time analytics on streaming data and embedding R functionality in tools like Spotfire for business intelligence. Examples are provided of using TERR for predictive maintenance of oil and gas equipment and transportation logistics optimization.
Applying R in BI and Real Time applications EARL London 2015Lou Bajuk
Overview of the challenges of applying R in enterprise analytic applications, and TIBCO's approach to these challenges with Spotfire, TERR and Streambase.
Extending the R language to BI and Real-time Applications JSM 2015Lou Bajuk
TERR is TIBCO's enterprise-grade R engine that allows companies to leverage R's powerful analytics while overcoming limitations of the open source R engine. TERR enables embedding R functionality tightly within enterprise applications like Spotfire for enhanced analytics. It also allows deploying R models and code in production environments like streaming applications. TERR extends the reach of R across organizations in both interactive and automated analytics.
Using the R Language in BI and Real Time Applications (useR 2015)Lou Bajuk
R provides tremendous value to statisticians and data scientists, however they are often challenged to integrate their work and extend that value to the rest of their organization. This presentation will demonstrate how the R language can be used in Business Intelligence applications (such as Financial Planning and Budgeting, Marketing Analysis, and Sales Forecasting) to put advanced analytics into the hands of a wider pool of decisions makers. We will also show how R can be used in streaming applications (such as TIBCO Streambase) to rapidly build, deploy and iterate predictive models for real-time decisions. TIBCO's enterprise platform for the R language, TIBCO Enterprise Runtime for R (TERR) will be discussed, and examples will include fraud detection, marketing upsell and predictive maintenance.
Real time applications using the R LanguageLou Bajuk
TIBCO Enterprise Runtime for R (TERR) allows for real-time analytics using the R language within TIBCO's Complex Event Processing platforms. TERR provides a faster R engine that can be embedded in TIBCO products like Spotfire and CEP workflows. This enables rapid prototyping and deployment of predictive models to monitor real-time data streams and trigger automated actions. Example use cases discussed include predictive maintenance, customer loyalty analytics, and severe weather alert tracking.
Slides from my 12/10/14 Webinar with the Bloor Group on the importance of an Analytics Platform for delivering value across your organization, and how TIBCO Spotfire meets that need.
Presentation given at the Joint Statistical Meetings in Boston in Aug. 2014, on applications of the R language using TERR, in Business Intelligence and Real Time applications
As the number of packages available for R continues to grow, maintaining and testing these packages becomes more difficult. This difficulty is compounded as independent implementations of the R language, such as TIBCO Enterprise Runtime for R (TERR), are developed. To address this, we have created a test automation framework for testing packages with both TERR and R. We will describe how the framework automatically creates tests from a package's source files. Issues with testing on multiple platforms will be discussed. Suggestions for improving packages with tests will also be presented.
The Compatibility Challenge:Examining R and Developing TERRLou Bajuk
Slides from Michael Sannella, architect for TIBCO Enterprise Runtime for R (TERR), on the the Compatibility Challenge: Examining R and Developing TERR. Presented at useR 2014
Deploying R in BI and Real time ApplicationsLou Bajuk
Overview of how Spotfire and TERR enables the deployment of R language analytics into Business Intelligence and Real time applications, including several examples. Presented at useR 2014 at UCLA on 7/2/14
Extending the Reach of R to the Enterprise with TERR and SpotfireLou Bajuk
An overview of how TIBCO integrates dynamic, interactive visual applications in Spotfire with predictive and advanced analytics in the R language, using TIBCO Enterprise Runtime for R--our R-compatible, enterprise-grade platform for the R language.
Sannella use r2013-terr-memory-managementLou Bajuk
This document discusses memory management in TIBCO's Enterprise Runtime for R (TERR). It summarizes key differences between TERR and R in how they represent data objects, handle reference counting, and perform garbage collection. TERR uses abstract C++ classes to represent data with multiple possible representations. It also implements more precise 16-bit reference counting and leverages reference counts to immediately reclaim temporary objects, reducing the need for garbage collection compared to R.
Extend the Reach of R to the Enterprise (for useR! 2013)Lou Bajuk
An overview of how and why we developed TIBCO Enterprise Runtime for R (TERR), and how it helps organizations leverage the power of the R language more widely.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling ExtensionsPeter Muessig
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
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✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.