How to Use a Semantic Layer on Big Data to Drive AI & BI Impact

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Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization. This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer. You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights. Attend this session to learn about: - Gaining business alignment and reducing data prep for your AI and BI teams. - Making a consistent set of business metrics “analytics-ready” and accessible. - Accelerating end-to-end query performance while optimizing cloud resources. - Treating “data as a product” and how to drive business value for all consumers.

How to use a semantic
layer on Big Data to
drive AI & BI impact
David P. Mariani, CTO & Founder
Dataversity

A semantic layer provides
the agility, consistency,
and control needed to
scale enterprise BI and AI
3

What a Semantic Layer looks like in real life
4
The Semantic Model Tableau Excel Power BI

What does a Semantic Layer do?
CONSUMPTION INTEGRATION
DATA INTEGRATION
Connect consumption tools to data LIVE
Connect to data platforms, abstract location & format of data
SEMANTIC
MODELING
MULTI-DIMENSIONAL
CALCULATION ENGINE
PERFORMANCE
OPTIMIZATION
ANALYTICS
GOVERNANCE
Define business-friendly
data models
Support complex, cell-based
expressions
Optimize performance and
cost
Enforce access control and
data policies
5

How is a
semantic layer
used?
CLOUD ANALYTICS OPTIMIZATION
Speed of thought analytics on live
cloud data, agile data integration with
minimal data movement.
ENTERPRISE METRIS STORE
Single source of governed enterprise
metrics that can be self-served from
any AI/BI tool.
BRIDGING AI AND BI
Common view of data assets and
platform for publishing AI-generated
insights to decision makers.
OLAP MODERNIZATION
Migrate legacy OLAP (e.g. SSAS)
models to modern cloud-first
infrastructure.
6

Where does a semantic layer fit in the stack?
7
DATA CONSUMPTION
SEMANTIC LAYER
DATA API LAYER
DATA PREPARATION
RAW DATA
BI TOOLS AI/ML TOOLS APPLICATIONS
CLOUD DATA
WAREHOUSE
DATA LAKE
ENGINES
SAAS APIs
DATA
TRANSFORMATION
TOOLS
DATA LAKES
SAAS
APPLICATIONS
DATA
CATALO
G

The Value a
Semantic Layer
Provides
Control the Complexity
and Cost of Analytics
Simplify & Accelerate
Self-service
Provide Consistency &
Governance for AI & BI
8

TPC-DS 10TB Benchmark: Improvements with AtScale
1. Elapsed time for executing 1 query five times
2. Elapsed time executing 1 (x5), 5, 25, 50 queries
3. Compute costs for cluster time (Redshift, Snowflake) or bytes read (BigQuery) for user concurrency test
4. Complexity score for SQL queries for number of: functions, operations, tables, objects & subqueries (AtScale = 258, TPC-DS = 1,057)
Test
Improvement Factor with AtScale
BigQuery Redshift Snowflake Synapse
Query
Performance1 4x Faster 11x Faster 3x Faster 6x Faster
User Concurrency2 11x Faster 31x Faster 12x Faster 11x Faster
Improved ROI3 3x Better 4x Better 16x Better 2x Better
Complexity4 76% less complex SQL queries
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Real-time analytics is important for data-driven applications. Ampool provides an active data store (ADS) that can ingest data in real-time, analyze it using various engines, and serve the results concurrently. This eliminates "data blackout periods" and enables applications to use up-to-date information. Ampool's ADS is powered by Apache Geode and has connectors for ingesting and processing data. It supports both transactional and analytical workloads in memory for low-latency.

Migrating legacy ERP data into Hadoop

DataWorks Summit

Legacy ERP architecture offers an incredibly, efficient means of operational resource management, but a real challenge comes from extracting business insights from them. Over the past 30 years, ERP data system, such as SAP, can be hard to interact with especially at the source database level. Whether initial translation of business logic and hierarchies create significant customizations, as well as, merging those changes into analytical applications. Overall, the entire process of designing self-service reporting with business level context can be quite cumbersome, looking at an example platform like, SAP, which contain pre-packaged modules (MM, SD, PP, etc), integrating these systems into a series of pre-built analytics. The orchestration and integration over a wide range of open source technology solutions with some commercial CDC and reporting solutions into a reference solution that mimics several real customer scenarios today, living on relational platforms. Key considerations of extracting from the operational system of record, especially the merging of multiple systems in different time zones, will be addressed. Furthermore, the integration concerns of an analytics Hadoop platform, using HIVE Acid and Merge, as well as, flattening techniques for dimensional models. Many times a customer is temporarily limited in the range of data their ERP can contain, and older data is often offloaded to secondary systems or cold archiving entities. That goes away, but the opportunities expanded with real-time reporting across all of history, and the expanded use cases with advanced machine learnings methods. Speakers Jordan Martz, Director of Tech Solutions, Attunity David Freriks, Technology Evangelist, Qlik

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Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

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Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.

Data at the Speed of Business with Data Mastering and Governance

DATAVERSITY

Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value. Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.

Exploring Levels of Data Literacy

DATAVERSITY

What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it. In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen? This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

DATAVERSITY

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Make Data Work for You

DATAVERSITY

Uncover how your business can save money and find new revenue streams. Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how? By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability. In this webinar, you'll learn: - How your company can leverage data and AI to reduce spending and costs - Ways you can monetize data and AI and uncover new growth strategies - How different companies have implemented these strategies to achieve cost optimization benefits

Data Catalogs Are the Answer – What is the Question?

DATAVERSITY

Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

Data Catalogs Are the Answer – What Is the Question?

DATAVERSITY

Data Modeling Fundamentals

DATAVERSITY

Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Showing ROI for Your Analytic Project

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

DATAVERSITY

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

Data Governance Trends - A Look Backwards and Forwards

DATAVERSITY

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

1) The document discusses best practices for data protection on Google Cloud, including setting data policies, governing access, classifying sensitive data, controlling access, encryption, secure collaboration, and incident response. 2) It provides examples of how to limit access to data and sensitive information, gain visibility into where sensitive data resides, encrypt data with customer-controlled keys, harden workloads, run workloads confidentially, collaborate securely with untrusted parties, and address cloud security incidents. 3) The key recommendations are to protect data at rest and in use through classification, access controls, encryption, confidential computing; securely share data through techniques like secure multi-party computation; and have an incident response plan to quickly address threats.

2023 Trends in Enterprise Analytics

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

Data Strategy Best Practices

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Who Should Own Data Governance – IT or Business?

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Data Management Best Practices

DATAVERSITY

This document summarizes a research study that assessed the data management practices of 175 organizations between 2000-2006. The study had both descriptive and self-improvement goals, such as understanding the range of practices and determining areas for improvement. Researchers used a structured interview process to evaluate organizations across six data management processes based on a 5-level maturity model. The results provided insights into an organization's practices and a roadmap for enhancing data management.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

Keeping the Pulse of Your Data – Why You Need Data Observability to Improve D...

DATAVERSITY

This document discusses the importance of data observability for improving data quality. It begins with an introduction to data observability and how it works by continuously monitoring data to detect anomalies and issues. This is unlike traditional reactive approaches. Examples are then provided of how unexpected data values or volumes could negatively impact downstream processes but be resolved quicker with data observability alerts. The document emphasizes that data observability allows issues to be identified and addressed before they become costly problems. It promotes data observability as a way to proactively improve data integrity and ensure accurate, consistent data for confident decision making.

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Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

Data at the Speed of Business with Data Mastering and Governance

Exploring Levels of Data Literacy

Building a Data Strategy – Practical Steps for Aligning with Business Goals

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Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

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The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

Emerging Trends in Data Architecture – What’s the Next Big Thing?

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2023 Trends in Enterprise Analytics

Data Strategy Best Practices

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原版一模一样【微信：741003700 】【(heriotwatt学位证书)英国赫瑞瓦特大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理(heriotwatt学位证书)英国赫瑞瓦特大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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How to Use a Semantic Layer on Big Data to Drive AI & BI Impact

1. How to use a semantic layer on Big Data to drive AI & BI impact David P. Mariani, CTO & Founder Dataversity

2. 2

3. A semantic layer provides the agility, consistency, and control needed to scale enterprise BI and AI 3

4. What a Semantic Layer looks like in real life 4 The Semantic Model Tableau Excel Power BI

5. What does a Semantic Layer do? CONSUMPTION INTEGRATION DATA INTEGRATION Connect consumption tools to data LIVE Connect to data platforms, abstract location & format of data SEMANTIC MODELING MULTI-DIMENSIONAL CALCULATION ENGINE PERFORMANCE OPTIMIZATION ANALYTICS GOVERNANCE Define business-friendly data models Support complex, cell-based expressions Optimize performance and cost Enforce access control and data policies 5

6. How is a semantic layer used? CLOUD ANALYTICS OPTIMIZATION Speed of thought analytics on live cloud data, agile data integration with minimal data movement. ENTERPRISE METRIS STORE Single source of governed enterprise metrics that can be self-served from any AI/BI tool. BRIDGING AI AND BI Common view of data assets and platform for publishing AI-generated insights to decision makers. OLAP MODERNIZATION Migrate legacy OLAP (e.g. SSAS) models to modern cloud-first infrastructure. 6

7. Where does a semantic layer fit in the stack? 7 DATA CONSUMPTION SEMANTIC LAYER DATA API LAYER DATA PREPARATION RAW DATA BI TOOLS AI/ML TOOLS APPLICATIONS CLOUD DATA WAREHOUSE DATA LAKE ENGINES SAAS APIs DATA TRANSFORMATION TOOLS DATA LAKES SAAS APPLICATIONS DATA CATALO G

8. The Value a Semantic Layer Provides Control the Complexity and Cost of Analytics Simplify & Accelerate Self-service Provide Consistency & Governance for AI & BI 8

9. TPC-DS 10TB Benchmark: Improvements with AtScale 1. Elapsed time for executing 1 query five times 2. Elapsed time executing 1 (x5), 5, 25, 50 queries 3. Compute costs for cluster time (Redshift, Snowflake) or bytes read (BigQuery) for user concurrency test 4. Complexity score for SQL queries for number of: functions, operations, tables, objects & subqueries (AtScale = 258, TPC-DS = 1,057) Test Improvement Factor with AtScale BigQuery Redshift Snowflake Synapse Query Performance1 4x Faster 11x Faster 3x Faster 6x Faster User Concurrency2 11x Faster 31x Faster 12x Faster 11x Faster Improved ROI3 3x Better 4x Better 16x Better 2x Better Complexity4 76% less complex SQL queries 9

10. www.atscale.co m

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