Published By: Teradata
Published Date: Jan 30, 2015
Hadoop is the rising star of the business technology agenda for a simple reason — it disrupts the economics of data, analytics, and someday soon, all enterprise applications; it is secretly becoming an application platform too. Application development and delivery (AD&D) professionals should be aware of and take action on these eight predictions, including the disruptive power of “Hadooponomics,” Hadoop’s current killer app, the closing data management gap, and the emergence of brand new distros.
"In the paper, “Integrate Big Data into Your Business Processes and Enterprise Systems” you’ll learn how to drive maximum value with an enterprise approach to Big Data. Topics discussed include:
• How to ensure that your Big Data projects will drive clearly defined business value
• The operational challenges each Big Data initiative must address
• The importance of using an enterprise approach for Hadoop batch processing
TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.
The enterprise data warehouse (EDW) has been at the cornerstone of enterprise data strategies for over 20 years. EDW systems have traditionally been built on relatively costly hardware infrastructures. But ever-growing data volume and increasingly complex processing have raised the cost of EDW software and hardware licenses while impacting the performance needed for analytic insights. Organizations can now use EDW offloading and optimization techniques to reduce costs of storing, processing and analyzing large volumes of data.
Getting data governance right is critical to your business success. That means ensuring your data is clean, of excellent quality, and of verifiable lineage. Such governance principles can be applied in Hadoop-like environments. Hadoop is designed to store, process and analyze large volumes of data at significantly lower cost than a data warehouse. But to get the return on investment, you must infuse data governance processes as part of offloading.
Published By: Teradata
Published Date: May 01, 2015
Creating value in your enterprise undoubtedly creates competitive advantage. Making sense of the data that is pouring into the data lake, accelerating the value of the data, and being able to manage that data effectively is a game-changer. Michael Lang explores how to achieve this success in “Data Preparation in the Hadoop Data Lake.”
Enterprises experiencing success with data preparation acknowledge its three essential competencies: structuring, exploring, and transforming. Teradata Loom offers a new approach by enabling enterprises to get value from the data lake with an interactive method for preparing big data incrementally and iteratively.
As the first complete data management solution for Hadoop, Teradata Loom enables enterprises to benefit from better and faster insights from a continuous data science workflow, improving productivity and business value.
To learn more about how Teradata Loom can help improve productivity in the Hadoop Data Lake, download this report now.
This CIO eBook explores how to deploy Hadoop applications faster and easier with a workload automation solution that simplifies and automates Hadoop batch processing and connected enterprise workflows.
Read the eBook to learn:
• The role—and challenges—of Hadoop in Big Data application development
• Six considerations for a Hadoop proof-of-concept initiative
• How to connect Hadoop to other enterprise data sources and applications
In this white paper, you’ll discover an enterprise approach to Big Data that leverages workload automation to:
- Integrate Hadoop workflows into your enterprise processes to deliver new applications faster
- Resolve issues faster with predictive analytics, automated alerts, and early problem detection
- Achieve compliance and governance adherence
Integrieren Sie Ihrer Big Data Initiativen in Ihre Unternehmensweiten Geschäftsprozesse. Gerne machen wir Sie damit vertraut, wie sie mit Control-M für Hadoop die Anwendungsentwicklung beschleunigen und die Unternehmensintegration vereinfachen können. Besprochene Themen schließen folgende Punkte ein:
* Wie können Sie mit einem Enterprise Scheduler für Hadoop, weitere Automationsinseln vermeiden.
* Wie kann sichergestellt werden, dass Sie aus ihren Big Data Initiativen den gewünschten Mehrwert erhalten.
* Wie können Sie mit Ihren Big Data initiativen sich den administrativen Herausforderungen & Bedürfnissen stellen und mögliche Konfrontationen erfolgreich meistern.
Published By: Teradata
Published Date: Feb 26, 2013
This report explores the evolution of big data analytics and its maturity within the enterprise. It discusses the approaches and economics to using a Discovery platform and Apache Hadoop within the same unified analytical architecture.
Published By: Dell EMC
Published Date: Jun 29, 2016
IDC believes that EMC Isilon is indeed an easy to operate, highly scalable and efficient Enterprise Data Lake Platform. IDC validated that a shared storage model based on the Data Lake can in fact provide enterprise-grade service-levels while performing better than dedicated commodity off-the-shelf storage for Hadoop workloads.
Published By: Snowflake
Published Date: Jan 25, 2018
Compared with implementing and managing Hadoop (a traditional on-premises data warehouse) a data warehouse built for the cloud can deliver a multitude of unique benefits. The question is, can enterprises get the processing potential of Hadoop and the best of traditional data warehousing, and still benefit from related emerging technologies?
Read this eBook to see how modern cloud data warehousing presents a dramatically simpler but more power approach than both Hadoop and traditional on-premises or “cloud-washed” data warehouse solutions.
Learn about FlexPod Select with Hadoop and how this enterprise class Hadoop has validated, pre-configured components that allow for faster deployment, higher reliability, and smoother integration with your existing applications and infrastructure.
Published By: SnowFlake
Published Date: Jul 08, 2016
In the era of big data, enterprise data warehouse (EDW) technology continues to evolve as vendors focus on innovation and advanced features around in-memory, compression, security, and tighter integration with Hadoop, NoSQL, and cloud. Forrester identified the 10 most significant EDW software and services providers — Actian, Amazon Web Services (AWS), Hewlett Packard Enterprise (HPE), IBM, Microsoft, Oracle, Pivotal Software, SAP, Snowflake Computing, and Teradata — in the category and researched, analyzed, and scored them. This report details our findings about how well each vendor fulfills our criteria and where they stand in relation to each other to help enterprise architect professionals select the right solution to support their data warehouse platform.
Published By: Altiscale
Published Date: Oct 19, 2015
In this age of Big Data, enterprises are creating and acquiring more data than ever before. To handle the volume, variety, and velocity requirements associated with Big Data, Apache Hadoop and its thriving ecosystem of engines and tools have created a platform for the next generation of data management, operating at a scale that traditional data warehouses cannot match.
Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.