The Data Warehouse is NOT Dead
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Have you heard the hype that the Data Warehouse is dead? With technologies like the Data Lake and emerging data visualization tools continuing to evolve in the data space, enthusiasts are questioning whether conventional data layers like the data warehouse are still required to support your enterprise data strategy. While it may seem practical to move away from a data warehouse, it won’t be long before you start realizing the pitfalls of that approach. Like it or not, the data warehouse will continue to play an integral role in your organization’s Enterprise Information Architecture by ensuring actionable insights are being delivered with clean certified data. In this session, Kunal Sharma, senior enterprise architect at Sense Corp, will: Highlight the value of establishing a Clean Data Practice through governed data assets Make a distinction between what “Single Source of Data” and “Best Version of The Truth” mean for an organization Share uses cases for delivering certified data through a data warehouse Provide a conceptual viewpoint of Enterprise Data Architecture design Share an example of a modern analytics infrastructure platform
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First Utility is the largest independent energy supplier in the UK. They needed a platform to manage smart meter data at high volumes with high availability as smart meters could provide readings every 5-10 seconds. They used DataStax to build such a platform using DSE to store meter data in a distributed manner and provide a self-healing and monitoring system. This platform allows First Utility to provide customers insights into their energy usage and control over their smart meters.
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Big Data LDN 2018: TURNING MULTIPLE DATA LAKES INTO A UNIFIED ANALYTIC DATA L...
Date: 13th November 2018
Location: Governance and MDM Theatre
Time: 11:10 - 11:40
Organisation: TIBCO
About: The big data phenomenon continues to accelerate, resulting in multiple data lakes at most organisations. However, according to Gartner, “Through 2019, 90% of the information assets from big data analytic efforts will be siloed and unusable across multiple business processes.”
Are you ready to unleash this data from these silos and deliver the insights your organisation needs to drive compelling customer experiences, innovative new products and optimized operations? In this session you will learn how to apply data virtualisation to: - Access, transform and deliver data from across your lakes, clouds and other data sources - Empower a range of analytic users and tools with all the data they need - Move rapidly to a modern and flexible data architecture for the long run In addition, you will see a demonstration of data virtualisation in action.
Talend Summer 16 launch présentation: Open Data Preparation for Everyone
Everyone needs data to make smarter decisions. But data unleashed and ungoverned puts the whole enterprise at risk. With the latest Talend platform, you can empower anyone in your organization to put data to work, without risk.
Watch this on-presentation to learn about our latest innovations:
Self-service data access
IT-governed data usage with role-based security
Fully integrated with Talend Data Fabric
Plus, see what’s new from Talend to boost big data and cloud productivity and expand scalability.
Everything Has Changed Except Us: Modernizing the Data Warehouse
This document discusses modernizing data warehouse architecture to handle changes in data and analytics needs. It argues that the traditional data warehouse approach of fully modeling data before use is untenable with today's data volumes and rates of change. Instead, it advocates for a layered architecture that separates data acquisition, management, and delivery into independent but coordinated systems. This allows each layer and component to change at its own pace and focuses on data access and usability rather than strict control and governance. The goal is to design systems that can adapt to changes in data and analytics uses over time rather than trying to plan and control everything up front.
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Derfor skal du bruge en DataLake
DataLakes kan skalere i takt med skyen, nedbryde integrationsbarrierer og data gemt i siloer og bane vejen for nye forretningsmuligheder. Det er alt sammen med til at give et bedre beslutningsgrundlag for ledelse og medarbejdere. Kom og hør hvordan.
David Bojsen, Arkitekt, Microsoft
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Data Virtualization. An Introduction (ASEAN)
Watch full webinar here: https://bit.ly/3uiXVoC
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Watch on-demand this session to learn:
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MongoDB IoT City Tour STUTTGART: Hadoop and future data management. By, Cloudera
Bernard Doering, Senior Slaes Director DACH, Cloudera.
Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.
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:
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Transforming and Scaling Large Scale Data Analytics: Moving to a Cloud-based ...
The Census Bureau is the U.S. government's largest statistical agency with a mission to provide current facts and figures about America's people, places and economy. The Bureau operates a large number of surveys to collect this data, the most well known being the decennial population census. Data is being collected in increasing volumes and the analytics solutions must be able to scale to meet the ever increasing needs while maintaining the confidentiality of the data. Past data analytics have occurred in processing silos inhibiting the sharing of information and common reference data is replicated across multiple system. The use of the Hortonworks Data Platform, Hortonworks Data Flow and other open-source technologies is enabling the creation of a cloud-based enterprise data lake and analytics platform. Cloud object stores are used to provide scalable data storage and cloud compute supports permanent and transient clusters. Data governance tools are used to track the data lineage and to provide access controls to sensitive data.
Benefits of a data lake
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Opportunity: Data, Analytic & Azure
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Retrieving and managing data effectively is crucial to gain useful information and allow for the best decision making. But how long does it take you to get the information?
First, ETL time is usually a pain point as the size of data is often huge. Second, query time is critical as well, especially for ad hoc analyses.
Say goodbye to all this waiting!
Sadas Engine was specifically designed to achieve outstanding performances in DWH environments.
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DataStax
DataStax is a software company that provides the DataStax Enterprise platform for Cassandra, an open source, distributed database. DataStax Enterprise provides extreme data velocity, continuous availability, and operational simplicity. It has over 1,000 production deployments, 300 customers, and $84 million in funding. DataStax seeks to be the first viable alternative to Oracle for modern online applications and the first and best choice in databases.
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[DSC Europe 23] Milos Solujic - Data Lakehouse Revolutionizing Data Managemen...DataScienceConferenc1
We will dive into modern data management approaches that have become prevalent and popular across many industries, built on top of good old data lakes: Lakehouse. Here are some of the most common problems that are being solved with this novel approach: Data Silos Demolished: Discover how organizations are breaking down data silos that have plagued them for decades, unifying structured and unstructured data from diverse sources. Inefficient Data Processing: We'll unveil real-world examples of how inefficient data processing can grind productivity to a halt and explore how Data Lakehouses provide a powerful solution while improving governance and security. Real-time Analytics: Learn how modern businesses are striving to achieve real-time analytics and the role Data Lakehouses play in achieving this. Have one data copy that will serve BI, Reporting, and ML workloadsDay 1 (Lecture 1): Data Management- The Foundation of all Analytics
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Watch Paul's session from Fast Data Strategy on-demand here: https://goo.gl/3veKqw
"Through 2020, 50% of enterprises will implement some form of data virtualization as one enterprise production option for data integration" according to Gartner. It is clear that data virtualization has become a driving force for companies to implement an agile, real-time and flexible enterprise data architecture.
Attend this session to learn:
• What data virtualization actually means and how it differs from traditional data integration approaches
• The most important use cases and key patterns of data virtualization
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IBM Relay 2015: Open for Data
Modern apps and services are leveraging data to change the way we engage with users in a more personalized way. Skyla Loomis talks big data, analytics, NoSQL, SQL and how IBM Cloud is open for data.
Learn more by visiting our Bluemix Hybrid page: http://ibm.co/1PKN23h
Data Ninja Webinar Series: Realizing the Promise of Data Lakes
Watch the full webinar: Data Ninja Webinar Series by Denodo: https://goo.gl/QDVCjV
The expanding volume and variety of data originating from sources that are both internal and external to the enterprise are challenging businesses in harnessing their big data for actionable insights. In their attempts to overcome big data challenges, organizations are exploring data lakes as consolidated repositories of massive volumes of raw, detailed data of various types and formats. But creating a physical data lake presents its own hurdles.
Attend this session to learn how to effectively manage data lakes for improved agility in data access and enhanced governance.
This is session 5 of the Data Ninja Webinar Series organized by Denodo. If you want to learn more about some of the solutions enabled by data virtualization, click here to watch the entire series: https://goo.gl/8XFd1O
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If we learned anything in 2020, it’s that we need to be able to adapt. COVID-19 accelerated what was already a rapid pace of change. Every industry has been disrupted, and the digital experience is more important than ever. It is crucial to move from a digital tracked customer, to a digital engaged model, and finally, to a digital reimagined future.
In this webinar, our Transformation practice lead Michael Daehne, will share a view into the future of business and how to get ahead of the change. He will walk through 7 considerations to make sure you embrace the new order of business in your industry.
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4. Unbundle and Re-bundle the Value Chain
5. Explore the Power of the Platform
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Achieve New Heights with Modern Analytics
Businesses can leverage modern cloud platforms and practices for net-new solutions and to enhance existing capabilities, resulting in an upgrade in quality, increased speed-to-market, global deployment capability at scale, and improved cost transparency.
In this webinar, Josh Rachner, data practice lead at Sense Corp, will help prepare you for your analytics transformation and explore how to make the most on new platforms by:
Building a strong understanding of the rise, value, and direction of cloud analytics
Exploring the difference between modern and legacy systems, the Big Three technologies, and different implementation scenarios
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Why Data Science Projects Fail
AI can give your organization the competitive advantage it needs, but the alarming truth is that only 1 in 10 data science projects ever make it into production. To be successful, organizations must not only correctly design and implement data science, but also raise the data, numerical, and technology literacy across the business.
Attend this webinar to learn what common pitfalls you need to avoid to keep your data science projects from failing. Data Scientist Gaby Lio will engage with the audience about project dos and don’ts to ensure your project success. She will then walk through three client use cases to give examples of successful data projects at each stage in the journey to AI adoption.
Small Investments, Big Returns: Three Successful Data Science Use Cases
No journey is alike, and neither is the timeline of climbing towards full AI adoption. With varying ranges of technical capability and business readiness, one thing is for certain, you need to see results, and fast! In this webinar, we will explore three client use cases from the manufacturing industry, to oil and gas, to education with examples of successful projects including:
Sales Forecasting – We will share sales forecasting and market segmentation techniques in the manufacturing industry. Using historical sales data, we introduce fast and effective signal decomposition and clustering techniques to produce valuable customer insights.
Inventory Management – We apply text analytics and natural language processing techniques for advanced and custom automation. This use case saves significant time for inventory managers and analysts by accurately and rapidly classifying their inventory based on each item description.
Public Safety – We introduce a computer vision capability that can recognize firearms and trigger alerts. In this use case, we apply real-time object recognition technology for early detection of firearms for school safety.
You’ll walk away with modern analytics and AI tools to benefit your organization’s immediate needs no matter where you are on your journey to AI adoption.
10 Steps to Develop a Data Literate Workforce
Gartner had predicted that by 2020, 80% of organizations would initiate deliberate competency development in the field of data literacy to overcome extreme deficiencies. This has become even more critical to businesses today as they seek to adjust to the remote settings of the COVID-19 pandemic.
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In this webinar, Alissa Schneider, Sense Corp data governance leader, will examine the fundamentals of data literacy, why it’s important in today’s marketplace, and share the 10 steps you can take to enhance the data literacy in your organization.
Contact us for more information: https://sensecorp.com/business-consulting-contact/
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The document discusses why 87% of data science projects fail to make it into production. It identifies three main reasons for failure: data is inaccurate, siloed and slow; there is a lack of business readiness; and operationalization is unreachable. To address these issues, the document recommends establishing data governance, defining an organizational data science strategy and use cases, ensuring the technology stack is updated, and having data scientists collaborate with data engineers. It also provides tips for successful data science projects, such as having short timelines, small focused teams, and prioritizing business problems over solutions.
Managing Large Amounts of Data with Salesforce
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Salesforce’s ability to handle large workloads and participate in high-consumption, mobile-application-powering technologies continues to evolve. Pub/sub-models and the investment in adjacent properties like Snowflake, Kafka, and MuleSoft, has broadened the development scope of Salesforce. Solutions now range from internal and in-platform applications to fueling world-scale mobile applications and integrations. Unfortunately, guidance on the extended capabilities is not well understood or documented. Knowing when to move your solution to a higher-order is an important Architect skill.
In this webinar, Paul McCollum, UXMC and Technical Architect at Sense Corp, will present an overview of data and architecture considerations. You’ll learn to identify reasons and guidelines for updating your solutions to larger-scale, modern reference infrastructures, and when to introduce products like Big Objects, Kafka, MuleSoft, and Snowflake.
Infographic data
Three-quarters of organizations are leveraging data insights to make decisions, but data quality issues are holding many of them back. While 93% see data as a valuable asset, only half have a clearly defined data strategy, and most have not had one for over a year. The majority believe financial results will be negatively impacted within two years if data initiatives are not completed due to continuing data quality problems.
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Overview
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The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
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The Data Warehouse is NOT Dead
- 1. “THE DATA WAREHOUSE IS NOT DEAD!” A PRACTICAL GUIDE TO MODERN ENTERPRISE INFORMATION ARCHITECTURE
- 2. 2 Specialist, Commercial Division Austin, TX ksharma@sensecorp.com Kunal Sharma About the Presenter 15+ years leading complex data transformation projects for Fortune 500 and mid-size companies Clean Data Practice Leader
- 3. • Mainframes • Data Entry • Basic Reporting • Primitive Databases 1970s • Personal Computers • Business Applications • Relational Databases • Business Data Warehouse 1980s • Internet • Centralized Data Storage • Kimball and Inmon Data Modeling Theory • EDW Architecture Model 1990s • Big Data • Data Lakes & Hadoop • Cloud Computing • AI / ML • IoT / Telematics • Data Governance 2010s • Broadband = More Data • Business Intelligence • Data Mining and Predictive Modeling • SaaS • MDM 2000s A BRIEF HISTORY OF THE ENTERPRISE DATA WAREHOUSE
- 4. Source: Kellogg School of Management at Northwest University
- 5. THE VALUE OF CLEAN DATA DIRTY DATA CAN LEAD TO COSTLY DECISIONS
- 6. The Impact of Clean Data While we know that dirty water can impact the health of people, We don’t as easily accept or recognize that dirty data can impact the health of companies..
- 7. The Problem of Bad Data
- 8. Building a Clean Data Practice Establishing a Clean Data Practice is dependent upon a strong foundational Data Platform
- 9. THE RIGHT REASONS CONSIDERATIONS FOR YOUR DATA PLATFORM
- 10. Use Case Considerations Compliance Reporting Governed data produces certified results that ensure no miscues in both internal and external reporting Impact Analysis Change management can easily trace and identify any impacts to data consumers Digital Transformation Architecture should leverage a hub and spoke model to enable domain based micro service builds System Replacement Converting to a new system should leverage clean data as part of any data import activities Growth By Acquisition Requires a data strategy that supports a consolidated view of data across multiple data sources
- 11. MAKING THE DISTINCTION SINGLE SOURCE OF TRUTH VS BEST VERSION OF TRUTH
- 12. Making the Distinction Single Source of Truth Best Version of Truth Data storage principle to always source information from a single source Multiple sources of similar data across transactional systems Enables transparency, traceability, and clear ownership of the data Impacts timeliness and completeness of enterprise data Data usage principle for a single agreed upon view of data Requires a governed Master Data Management stewardship Results in certified “trusted” data for all data consumption needs Utilize business rules to eliminate data redundancy and define metrics
- 13. ENTERPRISE DATA ARCHITECTURE BUILDING THE RIGHT DATA LAYERS
- 14. Enterprise Data Architecture DATA GOVERNANCE
- 15. Data Lake
- 16. Operational Data Store (ODS)
- 17. Data Mart
- 18. OLAP Cubes Defining Characteristics • Daily data latency at minimum • Structured by analytical consumer functions • Semantic Layer with accompanying aggregation(s) • Data cubes enable consumers to quickly slice, dice, and summarize data in a presentation tool Typical Data Consumers • Production Support • Presentation Tools • Reporting Analysts • Executives / Upper Management
- 19. MODERN INFRASTRUCTURE THE CLOUD LAKE HOUSE
- 21. Cloud Lake House Streaming Mobile Log Files IoT Social On-Premises Databases Files Data Warehouse SaaS Applications ERP DATA SOURCES DATA GOVERNANCE Data Catalog | Master & Reference Data Management | Policies & Procedures DATA SECURITY User Provisioning | Protected Information | Network Access CLOUD DATA LAKE Raw Zone Structured Zone Curated Zone ANALYTICS SANDBOX Data Scientists CLOUD DATA WAREHOUSE Data Marts ODS OLAP Cubes CONSUMERS Data Analysts Presentation Tools Business Users APIs & Extracts CLOUD STORAGE STREAM PROCESSING BATCH PROCESSING
- 22. Utilize the opportunity to hit the reset button Planning For Modernization Data Governance is critical to your success Avoid the pitfalls of a “lift and shift then fix” migration Start small with a focus to maximize data enrichment Take advantage of the ecosystem to avoid vendor lock
- 23. Thanks For Joining Us We hope you enjoyed the presentation. If you’d like to learn more about The Clean Data Initiative, we encourage you to download the full eBook. DOWNLOAD EBOOK www.sensecorp.com | marketing@sensecorp.com
- 24. Q&A