Meet the experts dwo bde vds v7

Stream based Data Integration

Organising the Data Lake - Information Management in a Big Data World

This document discusses organizing data in a data lake or "data reservoir". It describes the changing data landscape with multiple platforms for different analytical workloads. It outlines issues with the current siloed approach to data integration and management. The document introduces the concept of a data reservoir - a collaborative, governed environment for rapidly producing information. Key capabilities of a data reservoir include data collection, classification, governance, refinery, consumption, and virtualization. It describes how a data reservoir uses zones to organize data at different stages and uses workflows and an information catalog to manage the information production process across the reservoir.

Traditional BI vs. Business Data Lake – A Comparison

Centrica supplies energy to 28 million customers globally. It is developing integrated energy solutions for commercial and industrial customers through its Distributed Energy & Power division. Centrica created Io-Tahoe to provide a new operational model for data management that empowers businesses and IT to innovate using data. Io-Tahoe ingests diverse data sources into Centrica's data lake and uses smart data discovery and metadata management to create a known data model. This allows Centrica to extract more value from data through data science and gain business insights.

Hadoop India Summit, Feb 2011 - Informatica

Sanjeev Kumar

Capgemini Insights and Data

This document discusses strategies for transitioning from a traditional data warehousing architecture to a modern data architecture. It outlines a 4 sprint approach including developing social sensing capabilities, integrating additional data sources, implementing statistical and machine learning methods, and designing an operating model. It emphasizes the importance of a "kill strategy" to decommission legacy systems, a user adoption strategy to transition users to the new system, and implementing a "data concierge" service to streamline data provisioning and maximize value from the new platform. The strategies described aim to rationalize costs, simplify the data landscape, and enable more agile analytics and business transformation.

Enterprise 360 - Graphs at the Center of a Data Fabric

Precisely

Data fabric architectures are used to simplify and integrate data management across business functions to accelerate digital transformation. Creating a data fabric is a way to develop a data-centric view of your business which results in an Enterprise 360 perspective based on trusted data. Industry analysts and vendors are increasingly finding that graph databases are a key enabling technology in support of Data Fabric architectures that deliver trusted data. During this on-demand webinar, we discuss how we help our customers implement a Data Fabric pattern using graph database technology in support of their key strategic objectives.

EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...

Rip and replace isn't a good approach to IT change. When looking at Hadoop, MPP, in-memory and predictive analytics the challenge is making them co-exist with current solutions. Learn how Capgemini’s Pivotal CoE utilizes Cloud Foundry and PivotalOne to help businesses adopt new technologies without losing the value of current investments. Presented by Michael Wood of Pivotal and Steve Jones, Global Director, Strategy, Big Data and Analytics, Capgemini, at EMC World 2014.

The Future of Data Management: The Enterprise Data Hub

The document discusses the future of data management through the use of an enterprise data hub (EDH). It notes that an EDH provides a centralized platform for ingesting, storing, exploring, processing, analyzing and serving diverse data from across an organization on a large scale in a cost effective manner. This approach overcomes limitations of traditional data silos and enables new analytic capabilities.

Smart data for a predictive bank

ING Bank has developed a data lake architecture to centralize and govern all of its data. The data lake will serve as the "memory" of the bank, holding all data relevant for reporting, analytics, and data exchanges. ING formed an international data community to collaborate on Hadoop implementations and identify common patterns for file storage, deep data analytics, and real-time usage. Key challenges included the complexity of Hadoop, difficulty of large-scale collaboration, and ensuring analytic data received proper security protections. Future steps include standardizing building blocks, defining analytical model production, and embedding analytics in governance for privacy compliance.

Delivering Self-Service Analytics using Big Data and Data Virtualization on t...

Watch full webinar here: [https://buff.ly/2FHWnMD] Headquartered in New York City, Guardian Life is one of the largest mutual life insurance companies in the United States. Guardian offerings range from life insurance, disability income insurance, annuities, and investments to dental and vision insurance and employee benefits. The Enterprise Data Program was initiated to modernize Guardian’s technology capabilities and transform how Guardian leverages data – the Enterprise Data Lake was implemented to democratize data and drive self-service analytics throughout the organization. Data virtualization has played a key role for delivering data services through Guardian’s Enterprise Data Marketplace, a centralized portal for analytics and reporting. Attend this session to learn: Who is Guardian and what were the key drivers for building a data lake? What are the data architectural patterns on the cloud? How data virtualization is powering analytics and reporting?

Unlocking data science in the enterprise - with Oracle and Cloudera

This document discusses unlocking data science in the enterprise with Cloudera Data Science Workbench. It introduces Cloudera Data Science Workbench as a tool that accelerates data science from development to production. It allows data scientists to use R, Python, or Scala from a web browser to directly access and analyze data stored in Hadoop clusters. Cloudera Data Science Workbench provides secure, self-service environments for data scientists while also giving IT control over security and compliance. The document includes a demo of Cloudera Data Science Workbench's features.

Emergence of MongoDB as an Enterprise Data Hub

MongoDB

Gov & Private Sector Regulatory Compliance: Using Hadoop to Address Requirements

DataWorks Summit

This presentation discusses forward-looking statements that are subject to risks and uncertainties. It addresses issues around who owns data, who has access to data, and what type of data analysis can be done. It provides details on government-to-government, bank-to-government, and regional data exchanges. It discusses Rante's divisions and approach to unique experiences. Rante aims to anticipate industry trends and push boundaries through research and technology innovations.

Active Governance Across the Delta Lake with Alation

Databricks

Unlocking Big Data Silos in the Enterprise or the Cloud (Con7877)

The document discusses Oracle Data Integration solutions for unifying big data silos in enterprises and the cloud. The key points covered include: - Oracle Data Integration provides data integration and governance capabilities for real-time data movement, transformation, federation, quality and verification, and metadata management. - It supports a highly heterogeneous set of data sources, including various database platforms, big data technologies like Hadoop, cloud applications, and open standards. - The solutions discussed help improve agility, reduce costs and risk, and provide comprehensive data integration and governance capabilities for enterprises.

The Future of Data Management: The Enterprise Data Hub

The document discusses the enterprise data hub (EDH) as a new approach for data management. The EDH allows organizations to bring applications to data rather than copying data to applications. It provides a full-fidelity active compliance archive, accelerates time to insights through scale, unlocks agility and innovation, consolidates data silos for a 360-degree view, and enables converged analytics. The EDH is implemented using open source, scalable, and cost-effective tools from Cloudera including Hadoop, Impala, and Cloudera Manager.

Enterprise Data Hub: The Next Big Thing in Big Data

If you missed Strata + Hadoop World, you missed quite a bit. This year's event was packed with Big Data practitioners across industries who shared their experiences and how they are driving new innovations like never before. Just because you weren't there, doesn't mean you missed out. In this session, we'll touch on a few of the key highlights from the show, including: Key trends in Big Data adoption The enterprise data hub How the enterprise data hub is used in practice

Informatica Becomes Part of the Business Data Lake Ecosystem

Informatica is now part of the Business Data Lake ecosystem developed by Capgemini and Pivotal. Customers worldwide will now be able to leverage Informatica’s data integration software in addition to Pivotal’s advanced big data, analytics and application software, and Capgemini’s industry and implementation expertise. Informatica will deliver certified technologies for Data Integration, Data Quality and Master Data Management (MDM) to help enterprises distill raw data into actionable insights. http://www.capgemini.com/resources/the-business-data-lake-delivering-the-speed-and-accuracy-to-solve-your-big-data-problems

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Logical Data Warehouse and Data Lakes

Logical Data Warehouse and Data Lakes can play a role in many different type of projects and, in this presentation, we will look at some of the most common patterns and use cases. Learn about analytical and big data patterns as well as performance considerations. Example implementations will be discussed for each pattern. - Architectural patterns for logical data warehouse and data lakes. - Performance considerations. - Customer use cases and demo. This presentation is part of the Denodo Educational Seminar, and you can watch the video here goo.gl/vycYmZ.

Developing a Strategy for Data Lake Governance

Tony Baer

Modern Data Management for Federal Modernization

Watch full webinar here: https://bit.ly/2QaVfE7 Faster, more agile data management is at the heart of government modernization. However, Traditional data delivery systems are limited in realizing a modernized and future-proof data architecture. This webinar will address how data virtualization can modernize existing systems and enable new data strategies. Join this session to learn how government agencies can use data virtualization to: - Enable governed, inter-agency data sharing - Simplify data acquisition, search and tagging - Streamline data delivery for transition to cloud, data science initiatives, and more

Affecto Informatica World Tour 2015: The Age of Engagement

Affecto

We are moving from an era of cost optimisation and productivity to an age of engagement. This engagement is with customers, partners, third parties, and machines. At the center of every winning business strategy is data. How to accelerate your organisation’s results using the Intelligent Data Platform to manage data of any type and from any source to drive better business outcomes. A presentation by Greg Hanson, Vice President Business Operations EMEA, Informatica

Idq summit2014 ronald damhof - it's all about the data

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What's hot

Open Source in the Energy Industry - Creating a New Operational Model for Dat...

DataWorks Summit

Hadoop India Summit, Feb 2011 - Informatica

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Capgemini Insights and Data

Enterprise 360 - Graphs at the Center of a Data Fabric

Precisely

EMC World 2014 Breakout: Move to the Business Data Lake – Not as Hard as It S...

The Future of Data Management: The Enterprise Data Hub

Smart data for a predictive bank

Delivering Self-Service Analytics using Big Data and Data Virtualization on t...

Unlocking data science in the enterprise - with Oracle and Cloudera

Emergence of MongoDB as an Enterprise Data Hub

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Gov & Private Sector Regulatory Compliance: Using Hadoop to Address Requirements

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Active Governance Across the Delta Lake with Alation

Databricks

Unlocking Big Data Silos in the Enterprise or the Cloud (Con7877)

The Future of Data Management: The Enterprise Data Hub

Enterprise Data Hub: The Next Big Thing in Big Data

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Logical Data Warehouse and Data Lakes

Developing a Strategy for Data Lake Governance

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What's hot (20)

Open Source in the Energy Industry - Creating a New Operational Model for Dat...

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Streaming real time data with Vibe Data Stream

InformaticaMarketplace

The process of streaming real-time data from a wide variety of machine data sources and entities can be very complex and unwieldy. Using an agent-based approach, Informatica has invented a new technique and open access product that makes this process much more user friendly and efficient, even when dealing with multiple environments such as Hadoop, Cassandra, Storm, Amazon Kinesis and Complex Event Processing.

Power Big Data Analytics with Informatica Cloud Integration for Redshift, Kin...

Amazon Web Services

Companies are dealing with increasingly large data sets and looking for ways to significantly improve the scale and cost of Big Data analysis with AWS. This hands-on session shows you how you can achieve that. With hundreds of pre-built connectors, you will learn how to get your on-premise and cloud data into Redshift in minutes, not days, and at a significantly reduced costs using Informatica Cloud Integration. With fully certified support for large scale RDS deployments and Informatica’s Vibe Data Stream solution for automated streaming data collection for Kinesis, Informatica offers a comprehensive cloud integration solution for Big Data analytics with AWS. The ability to seamlessly migrate Informatica’s PowerCenter to Amazon Cloud (EC2) offers customers a Cloud migration path, with even higher performance and lower costs.

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Ramy Mahrous

This document provides an overview and agenda for a presentation on Informatica Big Data and Social Media Integration. It discusses Informatica and its platform, integration with Hadoop and big data, social media integration, and Informatica's telecom network streaming integration solution. The document includes information on Informatica's history and partnerships, how its platform addresses traditional ETL challenges, its features for working with Hadoop and big data, and how it can be used to integrate social media data and perform customer sentiment analysis.

Integrate Big Data into Your Organization with Informatica and Perficient

Perficient, Inc.

This document discusses how Perficient, an IT consulting firm, can help clients integrate big data into their organizations at lower total costs. It provides an overview of Perficient's services and solutions expertise in areas like business intelligence, customer experience, enterprise resource planning, and mobile platforms. The document also profiles Perficient with details on its history, locations, colleagues, and partnership model. Finally, it outlines an agenda for an event on balancing innovation and costs with big data, including discussions on PowerCenter Big Data Edition and what customers are doing with Informatica and big data.

Hadoop Operations - Best practices from the field

Uwe Printz

Discover Enterprise Security Features in Hortonworks Data Platform 2.1: Apach...

This document provides an overview of new security features in Hortonworks Data Platform (HDP) 2.1, including the Knox gateway for securing Hadoop REST APIs, extended access control lists (ACLs) in HDFS, and Apache Hive authorization using ATZ-NG. Knox provides a single access point and central security for REST APIs. Extended HDFS ACLs allow assigning different permissions to users and groups. Hive ATZ-NG implements SQL-style authorization with grants and revokes, integrating policies with the table lifecycle.

(BDT305) Lessons Learned and Best Practices for Running Hadoop on AWS | AWS r...

Amazon Web Services

Enterprises are starting to deploy large scale Hadoop clusters to extract value out of the data that they are generating. These clusters often span hundreds of nodes. To speed up the time to value, a lot of the newer deployments are happening in AWS, moving from the traditional on-premises, bare-metal world. Cloudera supports just such deployments. In this session, Cloudera shares the lessons learned and best practices for deploying multi-tenant Hadoop clusters in AWS. They will cover what reference deployments look like, what services are relevant for Hadoop deployments, network configurations, instance types, backup and disaster recovery considerations, and security considerations. They will also talk about what works well, what doesn't, and what has to be done going forward to improve the operability of Hadoop on AWS.

Hdp security overview

As Hadoop becomes a critical part of Enterprise data infrastructure, securing Hadoop has become critically important. Enterprises want assurance that all their data is protected and that only authorized users have access to the relevant bits of information. In this session we will cover all aspects of Hadoop security including authentication, authorization, audit and data protection. We will also provide demonstration and detailed instructions for implementing comprehensive Hadoop security.

Atlanta Data Science Meetup | Qubole slides

Qubole

Qubole is a cloud data analytics company founded in 2011 by former Facebook engineers. It provides a platform for interactive analytics on large datasets using Apache Spark and Presto on AWS. Qubole handles cluster management and scaling to enable self-service analytics without requiring Hadoop expertise. Customers span industries like advertising, healthcare, and retail and use Qubole for log analysis, machine learning, and business intelligence.

Qubole - Big data in cloud

Dmitry Tolpeko

Big Data Challenges and How to Overcome Them with Qubole - a Self-Service Platform for Big Data Analytics built on Amazon Web Services, Microsoft and Google Clouds. Storing, accessing, and analyzing large amounts of data from diverse sources and making it easily accessible to deliver actionable insights for users can be challenging for data driven organizations. The solution for customers is to optimize scaling and create a unified interface to simplify analysis. Qubole helps customers simplify their big data analytics with speed and scalability, while providing data analysts and scientists self-service access in Cloud. The platform is fully elastic and automatically scales or contracts clusters based on workload. We will try to overview main features, advantages and drawback of this platform.

7 Big Data Challenges and How to Overcome Them

Qubole

Big data architectures and the data lake

James Serra

The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include: - Defining top-down and bottom-up approaches to data management - Explaining what a data lake is and how Hadoop can function as the data lake - Describing how a modern data warehouse combines features of a traditional data warehouse and data lake - Discussing how federated querying allows data to be accessed across multiple sources - Highlighting benefits of implementing big data solutions in the cloud - Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (

Data quality and data profiling

Shailja Khurana

This document discusses data quality and data profiling. It begins by describing problems with data like duplication, inconsistency, and incompleteness. Good data is a valuable asset while bad data can harm a business. Data quality is assessed based on dimensions like accuracy, consistency, completeness, and timeliness. Data profiling statistically examines data to understand issues before development begins. It helps assess data quality and catch problems early. Common analyses include analyzing null values, keys, formats, and more. Data profiling is conducted using SQL or profiling tools during requirements, modeling, and ETL design.

Data quality architecture

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