Focus on Your Analysis, Not Your SQL Code

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This document discusses the challenges of using SQL for data analysis and introduces Alteryx as an alternative. It notes that SQL can be difficult to understand and repeat, while Alteryx allows users to see the full data workflow, perform transformations without coding, and access different data sources flexibly. The presentation includes an agenda, overview of Alteryx's benefits, and demonstration of its capabilities.

Technologie

Focus onYour Analysis, NotYour SQL Code
Beth Narrish, Senior Product Marketing Manager
Dan Hilton, Solutions Architect
Mar 7, 2017

Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential
Agenda
• SQL and SQL
Challenges
• Who is Alteryx
• Benefits ofWorkflow
• Demo
• Q&A
2

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
The legacy approach to analytics is often challenging for
organizations
3
Data Blending
Predictive &
Spatial Analytics
Production &
Output Creation
Data Access Prep
& Cleansing
Data integration tool Analytic coding App creationData quality tool
Slow, long process Multiple tools and
process steps
Expensive software
and labor
IT Staff Data Scientists Line-of-Business

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
And while many organizations are relying on spreadsheets for
data analysis, they are also using low-level SQL queries
4
Only
Harvard Business Review: Data Blending:A Powerful Method For Faster, Easier Decisions

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
• SQL is the standard language for relational
database management systems and has been for
40 years
• Source: American National Standards
Institute
• While NoSQL has gained popularity with
Hadoop, MongoDB and other sources, SQL is still
king
• Impala and Spark can utilize SQL
Why SQL?
5

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
But SQL can pose challenges for analysts and their colleagues
• Colleagues often cannot pick up SQL code
and understand how data was pulled
• Documenting steps is needed to repeat the
process when data changes
• Troubleshooting each step in a complex
query can be tedious
6

Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential
A Leading Platform for Self-Service DataAnalytics
7
Prep, blend, and analyze
all your data using a
repeatable workflow
Deliver deeper insights
in hours, not weeks
Deploy and share
analytics at scale

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
A Leading Platform for Self-Service DataAnalytics
8
Enrich
Prep & Blend Analyze
Input All Relevant Data
Share
OutputAll Popular Formats

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
Transparency
See data flow through the process, and
the actions performed at every step
10

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
SeamlessTransformations
Extract, transform and load data
without writing any code
Efficiency In Debugging,Testing
and Prototyping
Stop spending time writing code only
to get a null data set; understand
exactly what is happening at each
stage of your analytic process
11

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
WhyWorkflows?
Accessibility
Access structured, unstructured or
semi-structured data from virtually
any environment: SQL Server,
NoSQL databases, desktop, cloud,
Big Data environments, and more
Flexibility
Leverage existing SQL statements,
and develop new procedures
12

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
Conclusion
14

© 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial
Conclusion
15

See whatAlteryx can do for you!
http://www.alteryx.com/alteryx-for-sql
http://www.alteryx.com/product-training
16

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2022 Trends in Enterprise Analytics

DATAVERSITY

These slides - based on the webinar - shed light on how business stakeholders make the most of information from their big data environments and the requirements those stakeholders have to turn big data into business impact. Using recent big data end-user research from leading IT analyst firm Enterprise Management (EMA), data from Vertica’s recent benchmarks on SQL on Hadoop, and firsthand customer experiences, viewers will learn: - Use cases where end users around the world are using big data in their organizations - How maturity with big data strategies impact why and how business stakeholders use information from their big data environments - How Vertica empowers the use of information from big data environments

How Businesses use Big Data to Impact the Bottom Line

Enterprise Management Associates

Manufacturers have an abundance of data, whether from connected sensors, plant systems, manufacturing systems, claims systems and external data from industry and government. Manufacturers face increased challenges from continually improving product quality, reducing warranty and recall costs to efficiently leveraging their supply chain. For example, giving the manufacturer a complete view of the product and customer information integrating manufacturing and plant floor data, with as built product configurations with sensor data from customer use to efficiently analyze warranty claim information to reduce detection to correction time, detect fraud and even become proactive around issues requires a capable enterprise data hub that integrates large volumes of both structured and unstructured information. Learn how an enterprise data hub built on Hadoop provides the tools to support analysis at every level in the manufacturing organization.

Hadoop and Manufacturing

Cloudera, Inc.

DevOps + DataOps = Digital Transformation

Delphix

It seems as if the agile methods have lots of DD’s going on. BDD – Behavior Driven Development, ATDD – Acceptance Test Driven Development and several others. Adopting BDD allows for testing to be done as early as possible in the software development life cycle, promoting accurate testing, ensuring proper test coverage, and supporting the introduction of automation testing. One challenge is understanding the characteristics and benefits of “driven” approaches. Join Josh Eastman to discover ways that BDD can be employed to describe and test system behavior, user stories, and user acceptance. Josh will discuss Gherkin - the language to capture behavior descriptions and automation with cucumber and explore the four major values of BDD to ensure product quality, while driving time and cost savings. Leave this session with a ideas to reduce the ambiguity and miscommunications that occurs during development and testing.

Behavior Driven Development—A Guide to Agile Practices

TechWell

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The Journey to Success with Big Data

Cloudera, Inc.

A modern Big Data architecture involves extending your on-premises data management to AWS, implementing a data pipeline to stream real-time data into cloud data warehouse Amazon Redshift, perform data transformation, discovery, predictive analytics through machine learning, visualize complex information and be notified to respond to business events. This session is for APN Consulting Partners and organizations looking for ways to accelerate and modernize their Big Data projects. You will learn how to deploy and integrate AWS Services with Third-party Solutions in AWS Marketplace. Reduce your time to market by combining AWS services, open source software and ready-to-run on AWS solutions. Familiarity with Database technologies required. The session includes demonstrations and cooperative learning group activities.

GPSWKS301_Comprehensive Big Data Architecture Made Easy

Amazon Web Services

Comprehensive Big Data Analytics Architecture Made Easy - The AWS Marketplace...

Amazon Web Services

Analytic workloads and the ability to determine “what happened” are some of the most common use cases across enterprises today - helping you understand and adapt based on changing trends. However, for most businesses today, they are only able to see a piece of the story. Analytics are limited by the amount of data able to be stored and ultimately accessed, it’s time-intensive to bring in new datasets or fit unstructured data into rigid schemas, and user access is constrained to a select few who must already know the questions they’re trying to answer. It’s no surprise that big data is disrupting this modus operandi for analytics. A modern, Hadoop-based platform is designed to help businesses break free of these analytic limitations, providing a new kind of adaptive, high-performance analytic database. The recent release of Cloudera 5.8 continues to advance Cloudera Enterprise as the foundation for these analytic workloads. Join Justin Erickson, Senior Director of Product Management at Cloudera, and Andy Frey, Chief Technology Officer at Marketing Associates, as they discuss: -What technology is needed to build a modern analytic database with Hadoop -What’s new with Cloudera 5.8 -How to align your teams around agile analytics -Real world success from Marketing Associates -What’s next for Cloudera Enterprise’s Analytic Database

Building a Modern Analytic Database with Cloudera 5.8

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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.

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

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.

Data at the Speed of Business with Data Mastering and Governance

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.

Exploring Levels of Data Literacy

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.

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

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

Make Data Work for You

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 Catalogs Are the Answer – What Is the Question?

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

Data Modeling Fundamentals

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.

Showing ROI for Your Analytic Project

DATAVERSITY

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

How a Semantic Layer Makes Data Mesh Work at Scale

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.

Is Enterprise Data Literacy Possible?

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.

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DATAVERSITY

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

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 - A Look Backwards and Forwards

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

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.

2023 Trends in Enterprise Analytics

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

Data Strategy Best Practices

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

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

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.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

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1. Focus onYour Analysis, NotYour SQL Code Beth Narrish, Senior Product Marketing Manager Dan Hilton, Solutions Architect Mar 7, 2017

3. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial The legacy approach to analytics is often challenging for organizations 3 Data Blending Predictive & Spatial Analytics Production & Output Creation Data Access Prep & Cleansing Data integration tool Analytic coding App creationData quality tool Slow, long process Multiple tools and process steps Expensive software and labor IT Staff Data Scientists Line-of-Business

4. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial And while many organizations are relying on spreadsheets for data analysis, they are also using low-level SQL queries 4 Only Harvard Business Review: Data Blending:A Powerful Method For Faster, Easier Decisions

5. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial • SQL is the standard language for relational database management systems and has been for 40 years • Source: American National Standards Institute • While NoSQL has gained popularity with Hadoop, MongoDB and other sources, SQL is still king • Impala and Spark can utilize SQL Why SQL? 5

6. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial But SQL can pose challenges for analysts and their colleagues • Colleagues often cannot pick up SQL code and understand how data was pulled • Documenting steps is needed to repeat the process when data changes • Troubleshooting each step in a complex query can be tedious 6

7. Download a FREETrial: alteryx.com/trial© 2017 Alteryx, Inc. | Confidential A Leading Platform for Self-Service DataAnalytics 7 Prep, blend, and analyze all your data using a repeatable workflow Deliver deeper insights in hours, not weeks Deploy and share analytics at scale

8. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial A Leading Platform for Self-Service DataAnalytics 8 Enrich Prep & Blend Analyze Input All Relevant Data Share OutputAll Popular Formats

9. How Alteryx Helps SQL Coders 9

11. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial WhyWorkflows? SeamlessTransformations Extract, transform and load data without writing any code Efficiency In Debugging,Testing and Prototyping Stop spending time writing code only to get a null data set; understand exactly what is happening at each stage of your analytic process 11

12. © 2017 Alteryx, Inc. | Confidential Download a FREETrial: alteryx.com/trial WhyWorkflows? Accessibility Access structured, unstructured or semi-structured data from virtually any environment: SQL Server, NoSQL databases, desktop, cloud, Big Data environments, and more Flexibility Leverage existing SQL statements, and develop new procedures 12

13. Demonstration

16. See whatAlteryx can do for you! http://www.alteryx.com/alteryx-for-sql http://www.alteryx.com/product-training 16

Focus on Your Analysis, Not Your SQL Code

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Focus on Your Analysis, Not Your SQL Code