Read this checklist report, with results based on the Eckerson Group’s survey and the Business Application Research Center (BARC), on how companies using the cloud for data warehousing and BI has increased by nearly 50%. BI teams must address multiple issues including data delivery, security, portability and more before moving to the cloud for its infinite scalability and elasticity.
Read this report to understand all 7 seven considerations – what, how and why they impact the decision to move to the cloud.
How can enterprises overcome the issues that come with traditional data warehousing? Despite the business value that data warehouses can deliver, too often they fall short of expectations. They take too long to deliver, cost too much to build and maintain, and cannot keep pace with changing business requirements.
If this all rings a bell, check out Attunity’s knowledge brief on data warehouse automation with Attunity Compose. The solution automates the design, build, and deployment of data warehouses, data marts and data hubs, enabling more agile and responsive operation. The automation reduces time-consuming manual coding, and error-prone repetitive tasks. Read the knowledge brief to learn more about your options.
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions.
This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
Modernizing your data warehouse is one way to keep up with evolving business requirements and harness new technology. For many companies, cloud data warehousing offers a fast, flexible, and cost-effective alternative to traditional on-premises solutions.
This report sponsored by Google Cloud, TDWI examines the rise of cloud-based data warehouses and identifies associated opportunities, benefits, and best practices. Learn more about cloud data warehousing with strategic advice from Google experts.
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
Amazon Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. Organizations choose Amazon Redshift for its affordability, flexibility, and powerful feature set:
• Enterprise-class relational database query and management system
• Supports client connections with many types of applications, including business intelligence (BI), reporting, data, and analytics tools
• Execute analytic queries in order to retrieve, compare, and evaluate large amounts of data in multiple-stage operations
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications.
Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios.
Since starting to work with this technolog
Today’s businesses generate staggering amounts of data, and learning to get the most value from that data is paramount to success. Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on-demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics.
Just as Amazon Web Services (AWS) has transformed IT infrastructure to something that can be delivered on demand, scalably, quickly, and cost-effectively, Amazon Redshift is doing the same for data warehousing and big data analytics. Redshift offers a massively parallel columnar data store that can be spun up in just a few minutes to deal with billions of rows of data at a cost of just a few cents an hour. It’s designed for speed and ease of use — but to realize all of its potential benefits, organizations still have to configure Redshift for the demands of their particular applications.
Whether you’ve been using Redshift for a while, have just implemented it, or are still evaluating it as one of many cloud-based data warehouse and business analytics technology options, your organization needs to understand how to configure it to ensure it delivers the right balance of performance, cost, and scalability for your particular usage scenarios.
Since starting to work with this technology
A modern data warehouse is designed to
support rapid data growth and interactive analytics over a variety of relational, non-relational, and
streaming data types leveraging a single, easy-to-use interface. It provides a common architectural
platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling
organizations to derive deeper business insights.
Key elements of a modern data warehouse:
• Data ingestion: take advantage of relational, non-relational, and streaming data sources
• Federated querying: ability to run a query across heterogeneous sources of data
• Data consumption: support numerous types of analysis - ad-hoc exploration, predefined
reporting/dashboards, predictive and advanced analytics
IDC’s research has shown the movement of most IT workloads to the cloud in the coming years. Yet, with all the talk about enterprises moving to the cloud, some of them still wonder if such a move is really cost effective and what business benefits may result. While the answers to such questions vary from workload to workload, one area attracting particular attention is that of the data warehouse.
Many enterprises have substantial investments in data warehousing, with an ongoing cost to managing that resource in terms of software licensing, maintenance fees, operational costs, and hardware. Can it make sense to move to a cloud-based alternative? What are the costs and benefits? How soon can such a move pay itself off?
Download now to find out more.
Scalable data platforms such as Apache Hadoop offer unparalleled cost
benefits and analytical opportunities. IBM helps fully leverage the scale
and promise of Hadoop, enabling better results for critical projects and
key analytics initiatives. The end-to- end information capabilities of
IBM® Information Server let you better understand data and cleanse,
monitor, transform and deliver it. IBM also helps bridge the gap between
business and IT with improved collaboration. By using Information
Server “flexible integration” capabilities, the information that drives business
and strategic initiatives—from big data and point-of- impact analytics
to master data management and data warehousing—is trusted, consistent
and governed in real time.
Since its inception, Information Server has been a massively parallel
processing (MPP) platform able to support everything from small to very
large data volumes to meet your requirements, regardless of complexity.
Information Server can uniquely support th
Data and analytics have become an indispensable part of gaining and keeping a competitive edge. But many legacy data warehouses introduce a new challenge for organizations trying to manage large data sets: only a fraction of their data is ever made available for analysis. We call this the “dark data” problem: companies know there is value in the data they collected, but their existing data warehouse is too complex, too slow, and just too expensive to use. A modern data warehouse is designed to support rapid data growth and interactive analytics over a variety of relational, non-relational, and streaming data types leveraging a single, easy-to-use interface. It provides a common architectural platform for leveraging new big data technologies to existing data warehouse methods, thereby enabling organizations to derive deeper business insights.
Key elements of a modern data warehouse:
• Data ingestion: take advantage of relational, non-relational, and streaming data sources
• Federated q
s your information technology (IT) organization pressured to get more work done with fewer people or on a constricted budget? Do you need to make IT a competitive asset rather than a cost center? Does your business struggle with slow software applications or data that's too often unavailable? If you answered "yes" to any of these questions, it's time to take a close look at Oracle Exadata, the world's fastest database machine exclusively designed to run Oracle Database. It is the first database machine optimized for data warehousing, online transaction processing (OLTP), and database consolidation workloads as well as in-memory databases and database as a service (DBaaS).
"The forces that gave rise to data warehousing in the 1980s are just as important today. However, history reveals the benefits and drawbacks of the traditional data warehouse and how it falls short. This eBook explains how data warehousing has been re-thought and reborn in the cloud for the modern, data-driven organization."
If you’re considering your first or next data warehouse, this complimentary eBook explains the cloud data warehouse and how it compares to other data platforms.
Download Cloud Data warehouse for Dummies and learn how to get the most out of your data.
Highlights include:
What a cloud data warehouse is
Trends that brought about the adoption of cloud data warehousing
How the cloud data warehouse compares to traditional and noSQL offerings
How to evaluate different cloud data warehouse solutions
Tips for choosing a cloud data warehouse
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.
Data Warehousing in the Age of Artificial Intelligence This Ebook will guide you through building and deploying scalable, production-ready artificial-intelligence applications. Inside, you will find several artificial intelligence use cases, code samples to help you get started, and an outline of high throughput data processing architectures necessary for developing AI applications.
Every day, torrents of data inundate IT organizations and overwhelm
the business managers who must sift through it all to
glean insights that help them grow revenues and optimize
profits. Yet, after investing hundreds of millions of dollars into
new enterprise resource planning (ERP), customer relationship
management (CRM), master data management systems (MDM),
business intelligence (BI) data warehousing systems or big data
environments, many companies are still plagued with disconnected,
“dysfunctional” data—a massive, expensive sprawl of
disparate silos and unconnected, redundant systems that fail to
deliver the desired single view of the business.
To meet the business imperative for enterprise integration and
stay competitive, companies must manage the increasing variety,
volume and velocity of new data pouring into their systems from
an ever-expanding number of sources. They need to bring all
their corporate data together, deliver it to end users as quickly as
possible to maximize
As easy as it is to get swept up by the hype surrounding big data, its just as easy for organisations to become discouraged by the challenges they encounter while implementing a big data initiative. Concerns regarding big data skill sets (and the lack thereof), security, the unpredictability of data, unsustainable costs, and the need to make a business case can bring a big data initiative to a screeching halt.
However, given big data's power to transform business, it's critical that organisations overcome these challenges and realise the value of big data. The cloud can help organisations to do so. Drawing from IDG's 2015 Big Data and Analytics Survey, this white paper analyses the top five challenges companies face when undergoing a big data initiative and explains how they can effectively overcome them.
Every day, torrents of data inundate IT organizations and overwhelm
the business managers who must sift through it all to
glean insights that help them grow revenues and optimize
profits. Yet, after investing hundreds of millions of dollars into
new enterprise resource planning (ERP), customer relationship
management (CRM), master data management systems (MDM),
business intelligence (BI) data warehousing systems or big data
environments, many companies are still plagued with disconnected,
“dysfunctional” data—a massive, expensive sprawl of
disparate silos and unconnected, redundant systems that fail to
deliver the desired single view of the business.
Every day, torrents of data inundate IT organizations and overwhelm
the business managers who must sift through it all to
glean insights that help them grow revenues and optimize
profits. Yet, after investing hundreds of millions of dollars into
new enterprise resource planning (ERP), customer relationship
management (CRM), master data management systems (MDM),
business intelligence (BI) data warehousing systems or big data
environments, many companies are still plagued with disconnected,
“dysfunctional” data—a massive, expensive sprawl of
disparate silos and unconnected, redundant systems that fail to
deliver the desired single view of the business.
To meet the business imperative for enterprise integration and
stay competitive, companies must manage the increasing variety,
volume and velocity of new data pouring into their systems from
an ever-expanding number of sources. They need to bring all
their corporate data together, deliver it to end users as quickly as
possible to maximize
To compete in today’s fast-paced business climate, enterprises need
accurate and frequent sales and customer reports to make real-time
operational decisions about pricing, merchandising and inventory
management. They also require greater agility to respond to business
events as they happen, and more visibility into business activities so
information and systems are optimized for peak efficiency and performance.
By making use of data capture and business intelligence to
integrate and apply data across the enterprise, organizations can capitalize
on emerging opportunities and build a competitive advantage.
The IBM® data replication portfolio is designed to address these issues
through a highly flexible one-stop shop for high-volume, robust, secure
information replication across heterogeneous data stores.
The portfolio leverages real-time data replication to support high
availability, database migration, application consolidation, dynamic
warehousing, master data management (MDM), service
Obtaining a first-mover competitive advantage or faster time-to-market requires a new wave in analytics. Dassault Systèmes remains a leading innovator in Product Lifecycle Management (PLM) and has invested heavily in analytical technologies to further drive business benefits for its customers in the related areas of planning, simulation, insight and optimization.
This white paper examines the challenges peculiar to PLM and why Dassault Systèmes’ EXALEAD offers the most appropriate solution. It also clearly positions EXALEAD PLM Analytics alongside related technologies like BI, data-warehousing and Big Data solutions.
Understand and implement PLM Analytics to access actionable information, support accurate decision-making, and drive performance.
DB2 is a proven database for handling the most demanding transactional workloads. But the trend as of
late is to enable relational databases to handle analytic queries more efficiently by adding an inmemory
column store alongside to aggregate data and provide faster results. IBM's BLU Acceleration
technology does exactly that. While BLU isn't brand new, the ability to spread the column store across
a massively parallel processing (MPP) cluster of up to 1,000 nodes is a new addition to the technology.
That, along with simpler monthly pricing options and integration with dashDB data warehousing in the
cloud, makes DB2 for LUW, a very versatile database.
