The New Basics of Business Intelligence Lesson 1: Big Data Exploration

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Learn how to conquer the three biggest problems posed by big data: Using modern data platforms Making sense of massive volumes of data End-user adoption

Data & Analytics

FASTEST VISUAL ANALYTICS FOR BIG DATA
The New Basics of Business Intelligence
LESSON ONE: BIG DATA EXPLORATION

5 | Confidential
Traditional BI Diminishes Your ROI on Modern Systems

6 | Confidential
Traditional BI – Not Designed for High Volume (1 of 2)
Poor Performance • Monolithic queries • Time-to-value increases (bad) • Discourages users
... Query and Wait

7 | Confidential
Traditional BI – Not Designed for High Volume (2 of 2)
...
Reduce Volume • Extracts as “best practice” • Misses the big picture • Wasteful use of resources

8 | Confidential
Traditional BI – Not Designed for Velocity or Variety
Modern is more than just "big data":
• Live Streaming Data
• Unstructured & Semi-Structured
• Cloud
• Combining Data Sources
Traditional BI Fails. Back to silos:
• Specialty Applications
• Custom Code
• ETL Snapshots to JDBC-accessible
systems

Think Big
Process in place
Break it down

10 | Confidential Note: *Spark is embedded with Zoomdata. External spark cluster can be used for scalability.
Process in place

11 | Confidential Note: *Spark is embedded with Zoomdata. External spark cluster can be used for scalability.
Break it down

12 | Confidential
Zoomdata
~ 15 min
Query & Wait
Data Exploration Use Case
1 BILLION ROWS
Timeinseconds

13 | Confidential
Zoomdata Delivers Where Others Fail
This is the
only possible
solution to
ever-increasing
data volumes
and velocity

14 | Confidential
The New Basics of Business Intelligence: 1-2-3's
Encourage Data
Exploration
Speed-of-Thought
Performance
Connect to
Modern Data
A MODERN BUSINESS INTELLIGENCE PLATFORM
1 2 3

Demo 1 – Big Data Exploration
1 BILLION ROWS

Demo – Big Data Exploration
10 BILLION ROWS

FASTEST VISUAL ANALYTICS FOR BIG DATA
The New Basics of Business Intelligence
LESSON TWO: REAL TIME DATA VISUALIZATION | February 13, 2018

FASTEST VISUAL ANALYTICS FOR BIG DATA Thank You!
To register for other New
Basic Webinars:
http://go.zoomdata.com/the-new
-basics-webinar-series

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The goal of modern data warehousing is to not only deliver insights faster to more users, but provide a richer picture of your operations afforded by a greater volume and variety of data for analysis. This presentation from a Database Trends and Applications webcast will educate IT decision makers and data warehousing professionals about the must-have capabilities for modern data warehousing today – how they work and how best to use them.

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Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/2695mr Modernizing a data warehouse is no easy task. Digital Realty successfully accomplished this goal by using a data abstraction layer supported by real-time data virtualization. Now they are expanding this abstraction layer to support a MDM project to create a 360-degree of their customers. In this presentation, the VP of BI and Chief Information Architect at Digital Realty, Paul Balas presents: • The challenges associated with modernizing data warehouse • How to build a data abstraction layer using data virtualization • How to extend the abstraction layer to support MDM projects This session also includes a panel discussion with: • Paul Balas, VP of BI and Chief Information Architect at Digital Realty • Alberto Avila, Director IT Information and Collaboration Services at Cymer • Salah Kamel, CEO at Semarchy • Suresh Chandrasekaran, Sr. Vice President at Denodo (as moderator) This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6

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Amid the increasingly competitive brewing industry, the ability of retailers and brewers to provide optimal product assortments for their consumers has become a key goal for business stakeholders. Consumer trends, regional heterogeneities and massive product portfolios combine to scale the complexity of assortment selection. At AB InBev, we approach this selection problem through a two-step method rooted in statistical learning techniques. First, regression models and collaborative filtering are used to predict product demand in partnering retailers. The second step involves robust optimization techniques to recommend a set of products that enhance business-specified performance indicators, including retailer revenue and product market share. With the ultimate goal of scaling our approach to over 100k brick-and-mortar retailers across the United States and online platforms, we have implemented our algorithms in custom-built Python libraries using Apache Spark. We package and deploy production versions of Python wheels to a hosted repository for installation to production infrastructure. To orchestrate the execution of these processes at scale, we use a combination of the Databricks API, Azure App Configuration, Azure Functions, Azure Event Grid and some custom-built utilities to deploy the production wheels to on-demand and interactive Databricks clusters. From there, we monitor execution with Azure Application Insights and log evaluation metrics to Databricks Delta tables on ADLS. To create a full-fledged product and deliver value to customers, we built a custom web application using React and GraphQL which allows users to request assortment recommendations in a self-service, ad-hoc fashion.

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Tracking and analyzing how our individual products come together has always been an elusive problem for Steelcase. Our problem can be thought of in the following way: “we know how many Lego pieces we sell, yet we don’t know what Lego set our customers buy.” The Data Science team took over this initiative, which resulted in an evolution of our analytics journey. It is a story of innovation, resilience, agility and grit. The effects of the COVID-19 pandemic on corporate America shined the spotlight on office furniture manufacturers to solve for ways on which the office can be made safe again. The team would have never imagined how relevant our work on product application analytics would become. Product application analytics became an industry priority overnight. The proposal presented this year is the story of how data science is helping corporations bring people back to the office and set the path to lead the reinvention of the office space. After groundbreaking milestones to overcome technical challenges, the most important question is: What do we do with this? How do we scale this? How do we turn this opportunity into a true competitive advantage? The response: stop thinking about this work as a data science project and start to think about this as an analytics-enabled experience. During our session we will cover the technical elements that we overcame as a team to set-up a pipeline that ingests semi-structured and unstructured data at scale, performs analytics and produces digital experiences for multiple users. This presentation will be particularly insightful for Data Scientists, Data Engineers and analytics leaders who are seeking to better understand how to augment the value of data for their organization

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Enterprises are increasingly evolving their data infrastructures into entire cloud-facing environments. Interfacing private and public cloud data assets is a hallmark of initiatives such as logical data warehouses, data lakes and online transactional data hubs. These projects may involve deploying two or more of the following cloud-based data platforms into a hybrid architecture: Apache Hadoop, data warehouses, graph databases, NoSQL databases, multiworkload SQL databases, open source databases, data refineries and predictive analytics. Data application developers, data scientists and analytics professionals are driving their organizations’ efforts to bridge their data to the cloud. Several questions are of keen interest to those who are driving an organization’s evolution of its data and analytics initiatives into more holistic cloud-facing environments: • What is a hybrid cloud data services architecture? • What are the chief applications and benefits of a hybrid cloud data services architecture? • What are the best practices for bridging a logical data warehouse to the cloud? • What are the best practices for bridging advanced analytics and data lakes to the cloud? • What are the best practices for bridging an enterprise database hub to the cloud? • What are the first steps to take for bridging private data assets to the cloud? • How can you measure ROI from bridging private data to public cloud data services? • Which case studies illustrate the value of bridging private data to the cloud? Sign up now for a free 3-month trial of IBM Analytics for Apache Spark and IBM Cloudant, IBM dashDB or IBM DB2 on Cloud. http://ibm.co/ibm-cloudant-trial http://ibm.co/ibm-dashdb-trial http://ibm.co/ibm-db2-trial http://ibm.co/ibm-spark-trial

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Cybersecurity requires an organization to collect data, analyze it, and alert on cyber anomalies in near real-time. This is a challenging endeavor when considering the variety of data sources which need to be collected and analyzed. Everything from application logs, network events, authentications systems, IOT devices, business events, cloud service logs, and more need to be taken into consideration. In addition, multiple data formats need to be transformed and conformed to be understood by both humans and ML/AI algorithms. To solve this problem, the Aetna Global Security team developed the Unified Data Platform based on Apache NiFi, which allows them to remain agile and adapt to new security threats and the onboarding of new technologies in the Aetna environment. The platform currently has over 60 different data flows with 95% doing real-time ETL and handles over 20 billion events per day. In this session learn from Aetna’s experience building an edge to AI high-speed data pipeline with Apache NiFi.

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