Enterprise Data World 2018 - Building Cloud Self-Service Analytical Solution

This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.

Leading brands such as Pepsi and Macy’s use Celtra’s technology platform for brand advertising. To inform better product design and resolve issues faster, Celtra relies on Databricks to gather insights from large-scale, diverse, and complex raw event data. Learn how Celtra uses Databricks to simplify their Spark deployment, achieve faster project turnaround time, and empower people to make data-driven decisions. In this webinar, you will learn how Databricks helps Celtra to: - Utilize Apache Spark to power their production analytics pipeline. - Build a “Just-in-Time” data warehouse to analyze diverse data sources such as Elastic Load Balancer access logs, raw tracking events, operational data, and reportable metrics. - Go beyond simple counting and group events into sequences (i.e., sessionization) and perform more complex analysis such as funnel analytics.

The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.

View this presentation to learn about how to automate data pipelines for analytics from the experts at Microsoft and Attunity.

In this presentation, you will learn why data lineage is so important and what benefits data lineage provides.

Cortana Analytics Suite is a fully managed big data and advanced analytics suite that transforms your data into intelligent action. It is comprised of data storage, information management, machine learning, and business intelligence software in a single convenient monthly subscription. This presentation will cover all the products involved, how they work together, and use cases.

Amazon QuickSight is a business intelligence service that allows users to connect to data sources, create interactive dashboards, and securely share them across organizations. It offers auto-scaling, high availability, integration with AWS services, and pay-per-use pricing starting at $5/month for readers. QuickSight provides machine learning capabilities like anomaly detection and forecasting. It also allows embedding dashboards in applications. Customers like Capital One, Comcast, and the NFL use QuickSight for self-service analytics, embedded analytics, and delivering insights to large numbers of users through its reader role and usage-based pricing.

a talk about azure synapse aimed to help people who are not data experts understand what synapse is and how you can integrate it with other technologies

Azure Synapse is the evolution of Azure SQL Data Warehouse, combining big data, data storage and data integration into a single service for end-to-end cloud scale analytics. It provides unlimited analytics with unparalleled speed to gain insights. Azure Synapse brings together enterprise data warehousing and big data analytics to give a unified experience with the advantages of both worlds.

Enterprises are rapidly adopting stream computing backbones, in-memory data stores, change data capture, and other low-latency approaches for end-to-end applications. As businesses modernize their data architectures over the next several years, they will begin to evolve toward all-streaming architectures. In this webcast, Wikibon, Attunity, and MemSQL will discuss how enterprise data professionals should migrate their legacy architectures in this direction. They will provide guidance for migrating data lakes, data warehouses, data governance, and transactional databases to support all-streaming architectures for complex cloud and edge applications. They will discuss how this new architecture will drive enterprise strategies for operationalizing artificial intelligence, mobile computing, the Internet of Things, and cloud-native microservices. Link to the Wikibon report - wikibon.com/wikibons-2018-big-data-analytics-trends-forecast Link to Attunity Streaming CDC Book Download - http://www.bit.ly/cdcbook Link to MemSQL's Free Data Pipeline Book - http://go.memsql.com/oreilly-data-pipelines

May's RDX Insights Series Presentation focuses on Microsoft's BI products. We begin with an overview of Power BI, SSIS, SSAS and SSRS and how the products integrate with each other. The webinar continues with a detailed discussion on how to use Power BI to capture, model, transform, analyze and visualize key business metrics. We’ll finish with a Power BI demo highlighting some of its most beneficial and interesting features.

This document provides an overview of a speaker and their upcoming presentation on Microsoft's data platform. The speaker is a 30-year IT veteran who has worked in various roles including BI architect, developer, and consultant. Their presentation will cover collecting and managing data, transforming and analyzing data, and visualizing and making decisions from data. It will also discuss Microsoft's various product offerings for data warehousing and big data solutions.

This document discusses the future of data and the Azure data ecosystem. It highlights that by 2025 there will be 175 zettabytes of data in the world and the average person will have over 5,000 digital interactions per day. It promotes Azure services like Power BI, Azure Synapse Analytics, Azure Data Factory and Azure Machine Learning for extracting value from data through analytics, visualization and machine learning. The document provides overviews of key Azure data and analytics services and how they fit together in an end-to-end data platform for business intelligence, artificial intelligence and continuous intelligence applications.

A modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users.

This document summarizes a presentation given by Alberto Diaz Martin on Azure Databricks for data scientists. The presentation covered how Databricks can be used for infrastructure management, data exploration and visualization at scale, reducing time to value through model iterations and integrating various ML tools. It also discussed challenges for data scientists and how Databricks addresses them through features like notebooks, frameworks, and optimized infrastructure for deep learning. Demo sections showed EDA, ML pipelines, model export, and deep learning modeling capabilities in Databricks.

The document summarizes a presentation about data vault automation at a Dutch department store chain called de Bijenkorf. It discusses the project objectives of having a single source of reports and integrating with production systems. An architectural overview is provided, including the use of AWS services, a Snowplow event tracker, and Vertica data warehouse. Automation was implemented for loading data from over 250 source tables into the data vault and then into information marts. This reduced ETL development time and improved auditability. The data vault supports customer analysis, personalization, and business intelligence uses at de Bijenkorf. Drivers of the project's success included the AWS infrastructure, automation approach, and Pentaho ETL framework.

The document discusses using Attunity Replicate to accelerate loading and integrating big data into Microsoft's Analytics Platform System (APS). Attunity Replicate provides real-time change data capture and high-performance data loading from various sources into APS. It offers a simplified and automated process for getting data into APS to enable analytics and business intelligence. Case studies are presented showing how major companies have used APS and Attunity Replicate to improve analytics and gain business insights from their data.

The document provides an overview of big data concepts and Amazon Web Services (AWS) products for big data and analytics. It describes challenges of big data including unpredictable resource demand and job orchestration complexities. It then summarizes AWS products for data collection, storage, processing, analytics and machine learning. Specific examples are given using AWS services like Redshift, EMR, Kinesis and DynamoDB for scenarios like data warehousing, real-time streaming and Hadoop workloads. Core principles and common challenges of big data implementations on AWS are also outlined.

Billions of Rows Transformed in Record Time Using Matillion ETL for Amazon Redshift GE Power & Water develops advanced technologies to help solve some of the world’s most complex challenges related to water availability and quality. They had amassed billions of rows of data on on-premises databases, but decided to migrate some of their core big data projects to the AWS Cloud. When they decided to transform and store it all in Amazon Redshift, they knew they needed an ETL/ELT tool that could handle this enormous amount of data and safely deliver it to its destination. In this session, Ryan Oates, Enterprise Architect at GE Water, shares his use case, requirements, outcomes and lessons learned. He also shares the details of his solution stack, including Amazon Redshift and Matillion ETL for Amazon Redshift in AWS Marketplace. You learn best practices on Amazon Redshift ETL supporting enterprise analytics and big data requirements, simply and at scale. You learn how to simplify data loading, transformation and orchestration on to Amazon Redshift and how build out a real data pipeline. Get the insights to deliver your big data project in record time.

This document discusses techniques for optimizing Power BI performance. It recommends tracing queries using DAX Studio to identify slow queries and refresh times. Tracing tools like SQL Profiler and log files can provide insights into issues occurring in the data sources, Power BI layer, and across the network. Focusing on optimization by addressing wait times through a scientific process can help resolve long-term performance problems.

This presentation contains following slides, Introduction To OLAP Data Warehousing Architecture The OLAP Cube OLTP Vs. OLAP Types Of OLAP ROLAP V/s MOLAP Benefits Of OLAP Introduction - Apache Kylin Kylin - Architecture Kylin - Advantages and Limitations Introduction - Druid Druid - Architecture Druid vs Apache Kylin References For any queries Contact Us:- argonauts007@gmail.com

Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.

The document provides an overview of modern scalable web development trends. It discusses the motivation to build systems that can handle large amounts of data quickly and reliably. It then summarizes the evolution of software architectures from monolithic to microservices. Specific techniques covered include reactive system design, big data analytics using Hadoop and MapReduce, machine learning workflows, and cloud computing services. The document concludes with an overview of the technologies used in the Septeni tech stack.

The document discusses Socialmetrix's evolution of their real-time social media analytics architecture over 4 iterations to meet growing customer and data demands. It describes how they moved from a monolithic to distributed setup using technologies like AWS, Spark, Kafka and Cassandra to improve scalability, costs and resilience while adding new data sources and features. Key lessons included automating deployments, monitoring systems, and using AWS services like S3, EMR and DynamoDB to enable rapid prototyping and reprocessing as needed to support real-time and batch analytics.

Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020. Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms. Data lakes will be built in cloud object storage. We’ll discuss the options there as well. Get this data point for your data lake journey.

This document provides tips for optimizing performance in Power BI by focusing on different areas like data sources, the data model, visuals, dashboards, and using trace and log files. Some key recommendations include filtering data early, keeping the data model and queries simple, limiting visual complexity, monitoring resource usage, and leveraging log files to identify specific waits and bottlenecks. An overall approach of focusing on time-based optimization by identifying and addressing the areas contributing most to latency is advocated.

PCM18 (Big Data Analytics) slides of Pentaho Community Meetup in Bologna about Big Data OLAP using Pentaho, Vertica and Kylin

Working with big volumes of data is a complicated task, but it's even harder if you have to do everything in real time and try to figure it all out yourself. This session will use practical examples to discuss architectural best practices and lessons learned when solving real-time social media analytics, sentiment analysis, and data visualization decision-making problems with AWS. Learn how you can leverage AWS services like Amazon RDS, AWS CloudFormation, Auto Scaling, Amazon S3, Amazon Glacier, and Amazon Elastic MapReduce to perform highly performant, reliable, real-time big data analytics while saving time, effort, and money. Gain insight from two years of real-time analytics successes and failures so you don't have to go down this path on your own.

Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications. This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc

Serverless SQL provides a serverless analytics platform that allows users to analyze data stored in object storage without having to manage infrastructure. Key features include seamless elasticity, pay-per-query consumption, and the ability to analyze data directly in object storage without having to move it. The platform includes serverless storage, data ingest, data transformation, analytics, and automation capabilities. It aims to create a sharing economy for analytics by allowing various users like developers, data engineers, and analysts flexible access to data and analytics.