Priyank Patel, Teradata, Hadoop & SQL

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The document discusses Teradata's portfolio for Hadoop, including the Teradata Aster Big Analytics Appliance, the Teradata Appliance for Hadoop, a commodity offering with Dell, and support for the Hortonworks Data Platform. It provides consulting, training, support, and managed services for Hadoop. Teradata SQL-H gives business users standard SQL access to data stored in Hadoop through Teradata, allowing queries to run quickly on Teradata while accessing data from Hadoop efficiently through HCatalog.

1 8/8/2013 Teradata Confidential
AUDIO & VIDEO IMAGES TEXT WEB & SOCIAL MACHINE LOGS CRM SCM ERP
DISCOVERY
PLATFORM
CAPTURE | STORE | REFINE
INTEGRATED
DATA WAREHOUSE
LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS
Engineers
Data Scientists Business Analysts Marketing Front-Line Workers
Operational SystemsCustomers / Partners Executives
Aster Connector
for Hadoop
Aster
SQL-H
Aster Teradata
Connector
Teradata
SQL-H Teradata Connector
for Hadoop
Aster Loader Teradata Loader
TERADATA UNIFIED DATA ARCHITECTURE

2 8/8/2013 Teradata Confidential
Teradata Portfolio for Hadoop
”Taking Hadoop from Silicon Valley to Main Street”
Most Trusted & Flexible Hadoop Platforms for Your Next-Generation
Unified Data Architecture™
1. Teradata Aster Big Analytics Appliance
2. Teradata Appliance for Hadoop
3. Teradata Commodity Offering with Dell
4. Hortonworks Data Platform software-only support resell
Complete consulting and training capability
• Big Analytics Services – across the UDA
• Data Integration Optimization – ETL, ELT across the UDA
• Hadoop deployment & mentoring
• Teradata delivering Hortonworks training
• Hadoop Managed Services - operations & administration
Customer Support for Hadoop
• World-class Teradata customer support, backed by Hortonworks
What We Announced June 26th, 2013

Confidential and proprietary. Copyright © 2013 Teradata Corporation.3
Teradata SQL-H™
A Business User’s Bridge to Access Hadoop Data
Teradata SQL-H Gives Business
Users a Better Way to Access
Data Stored in Hadoop
• Trusted: Use existing tools/skills and
enable self-service BI with granular
security
• Allow standard ANSI SQL access to
Hadoop data
• Fast: Queries run on Teradata, data
accessed from Hadoop
• Efficient: Intelligent data access
leveraging the Hadoop HCatalog
Hadoop Layer: HDFS
Pig
Hive
Hadoop
MR
Teradata: SQL-H
HCatalog
Data
DataFiltering

Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.

Aster getting started

JESUS WORLD MISSION

The document introduces the Teradata Aster Discovery Platform for scalable analytics using analytic algorithms on commodity hardware. It discusses use cases like credit risk analysis, fraud detection, and sentiment analysis. It provides an overview of the discovery process model and instructions for downloading, installing, and using Aster including setting up the Aster Management Console and AsterLens for visualization. It then provides examples of using various Aster analytic functions like k-means clustering, market basket analysis, data unpacking, and nPath analysis for applications in marketing, pricing, and web analytics. It concludes that Aster provides more powerful analytics capabilities than SQL alone for exploring big data.

Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)

Rittman Analytics

Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.

Top Three Big Data Governance Issues and How Apache ATLAS resolves it for the...

DataWorks Summit/Hadoop Summit

This document provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.

Tapping into the Big Data Reservoir (CON7934)

Jeffrey T. Pollock

This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.

Expand a Data warehouse with Hadoop and Big Data

jdijcks

Teradata - Presentation at Hortonworks Booth - Strata 2014

Hortonworks

Ambari Meetup: 2nd April 2013: Teradata Viewpoint Hadoop Integration with Ambari

Hortonworks

Teradata Viewpoint provides a unified monitoring solution for Teradata Database, Aster, and Hadoop. It integrates with Ambari to simplify monitoring Hadoop. Viewpoint uses Ambari's REST APIs to collect metrics and alerts from Hadoop and store them in a database for trend analysis and visualization. This allows Viewpoint to deliver comprehensive Hadoop monitoring without having to understand its various monitoring technologies.

This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.

Filling the Data Lake

DataWorks Summit/Hadoop Summit

This document discusses strategies for filling a data lake by improving the process of data onboarding. It advocates using a template-based approach to streamline data ingestion from various sources and reduce dependence on hardcoded procedures. The key aspects are managing ELT templates and metadata through automated metadata extraction. This allows generating integration jobs dynamically based on metadata passed at runtime, providing flexibility to handle different source data with one template. It emphasizes reducing the risks associated with large data onboarding projects by maintaining a standardized and organized data lake.

Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...

NoSQLmatters

Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.

Oracle PL/SQL 12c and 18c New Features + RADstack + Community Sites

Steven Feuerstein

A Reference Architecture for ETL 2.0

DataWorks Summit

More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.

Navigating the World of User Data Management and Data Discovery

DataWorks Summit/Hadoop Summit

This document discusses navigating user data management and data discovery. It provides an overview of evaluating and selecting data management tools for a Hadoop data lake. Key criteria for evaluation include metadata curation, lineage and versioning, integration capabilities, and performance. Several vendors were evaluated, with Global ID, Attivio, and Waterline Data scoring highest based on the criteria. The presentation emphasizes selecting a limited number of tools based on business and user requirements.

Oracle Data Integration - Overview

Jeffrey T. Pollock

The document discusses Oracle's data integration products and big data solutions. It outlines five core capabilities of Oracle's data integration platform, including data availability, data movement, data transformation, data governance, and streaming data. It then describes eight core products that address real-time and streaming integration, ELT integration, data preparation, streaming analytics, dataflow ML, metadata management, data quality, and more. The document also outlines five cloud solutions for data integration including data migrations, data warehouse integration, development and test environments, high availability, and heterogeneous cloud. Finally, it discusses pragmatic big data solutions for data ingestion, transformations, governance, connectors, and streaming big data.

Oracle Data Integration CON9737 at OpenWorld

Jeffrey T. Pollock

Teradata Aster Discovery Platform

Scott Antony

Luo june27 1150am_room230_a_v2

DataWorks Summit

Near real-time, big data analytics is a reality via a new data pattern that avoids the latency and overhead of legacy ETL–the 3 T’s of Hadoop: Transfer, Transform, and Translate. Transfer: Once a Hadoop infrastructure is in place, a mandate is needed to immediately and continuously transfer all enterprise data, from external and internal sources and through different existing systems, into Hadoop. Previously, enterprise data was isolated, disconnected and monolithically segmented. Through this T, various source data are consolidated and centralized in Hadoop almost as they are generated in near real-time. Transform: Most of the enterprise data, when flowing into Hadoop, is transactional in nature. Analytics requires data be transformed from record-based OLTP form to column-based OLAP. This T is not the same T in ETL as we need to retain the granularity in the data feeds. The key is to transform in-place within Hadoop, without further data movement from Hadoop to other legacy systems. Translate: We pre-compute or provide on-the-fly views of analytical data, exposed for consumption. We facilitate analysis and reporting, for both scheduled and ad hoc needs, to be interactive with the data for analysts and end users, integrated in and on top of Hadoop.

Oracle Big Data Appliance and Big Data SQL for advanced analytics

jdijcks

Evolving Hadoop into an Operational Platform with Data Applications

DataWorks Summit

The document discusses Cask Data Application Platform (CDAP), an open source platform for building data applications on Hadoop. It provides an overview of CDAP's key components including datasets, programs, and applications. Datasets are standardized containers that encapsulate data access patterns and data models through reusable APIs. Programs are containers for different processing paradigms like batch and real-time. Applications in CDAP compose multiple datasets and programs.

Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic

DataWorks Summit

The document summarizes Mayo Clinic's implementation of a big data platform to process and analyze large volumes of daily healthcare data, including HL7 messages, for enterprise-wide clinical and non-clinical usage. The platform, built on Hadoop and using technologies like Storm and Elasticsearch, reliably handles 20-50 times more data than their current daily volumes. It provides ultra-fast free text search capabilities. The system supports applications like processing data for colorectal surgery, exceeding requirements and outperforming previous RDBMS-only systems. Ongoing work involves further enhancing capabilities and integrating with additional components as part of a unified data platform.

Hadoop Powers Modern Enterprise Data Architectures

DataWorks Summit

1) Hadoop enables modern data architectures that can process both traditional and new data sources to power business analytics and other applications. 2) By 2015, organizations that build modern information management systems using technologies like Hadoop will financially outperform their peers by 20%. 3) Hadoop provides an agile "data lake" solution that allows organizations to capture, process, and access all their data in various ways for business intelligence, analytics, and other uses.

Planing and optimizing data lake architecture

Milos Milovanovic

ETL big data with apache hadoop

Maulik Thaker

Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...

Hortonworks

This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.

Presentacin webinar move_up_to_power8_with_scale_out_servers_final

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Big Data: Architecture and Performance Considerations in Logical Data Lakes

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Optimize Data for the Logical Data Warehouse

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