Why Are Customers Architecting Hybrid Data Warehouses?

By Mona Patel

As a leader in IT, you may be  incented or mandated to explore cloud and big data solutions to transform rigid data warehousing environments into agile ones to match how the business really wants to operate.  The following questions must come to mind:

  • How do I integrate new analytic capabilities and data sets to my current on-premises data warehouse environment?
  • How do I deliver self service solutions to accelerate the analytic process?
  • How do I leverage commodity hardware to lower costs?

For these questions, and more, organizations are architecting hybrid data warehouses.  In fact, these organizations moving towards hybrid are referred to as ‘Best In Class’ according to The Aberdeen Group’s latest research: “Best In Class focus on hybridity, both in their data infrastructure and with their analytical tools as well.  Given the substantial investments companies have made in their IT environment, a hybrid approach allows them to utilize these investments to the best of their ability while explore more flexible and scalable cloud-based solutions as well.”  To hear more about these ‘Best In Class’ organizations, watch the 45 minute webcast.

How do you get to this hybrid data warehouse architecture with the least risk and most reward?  IBM dashDB delivers the most flexible, cloud database services to extend and integrate with your current analytics and data warehouse environment, addressing all the challenges related to leveraging new sources of customer, product, and operational insights to build new applications, products, and business models.

To help our clients evaluate hybrid data warehouse solutions, Harvard Research Group (HRG) provides an assessment of IBM dashDB.  In this paper, HRG highlights product functionality, as well as 3 uses cases in Healthcare, Oil and Gas, and Financial Services.   Security, Performance, High Availability, In-Database Analytics, and more are covered in the paper to ensure future architecture enhancements optimize IT rather than adding new skills, complexities, and integration costs. After reading this paper, you will find that dashDB enables IT to respond rapidly to the needs of the business, keep systems running smoothly, and achieve faster ROI.

To know more on dashDB check out the video below:

 

About Mona,

mona_headshotMona Patel is currently the Portfolio Marketing Manager for IBM dashDB, the future of data warehousing.  With over 20 years of analyzing data at The Department of Water and Power, Air Touch Communications, Oracle, and MicroStrategy, Mona decided to grow her career at IBM, a leader in data warehousing and analytics.  Mona received her Bachelor of Science degree in Electrical Engineering from UCLA.

Start Small and Move Fast: The Hybrid Data Warehouse

by Mona Patel

In the world of cutting edge big data analytics, the same obstacles in gaining meaningful insight still exists – ease of getting data in and getting data out.  To address these long standing issues, the utmost flexibility is needed, especially when layered with the agile needs of the business.

Why spend millions of dollars replacing your data and analytics environment with the latest technology promise to address these issues, when can you to leverage existing investments, resources, and skills to achieve the same, and sometimes better, insight?

Consider a hybrid data warehouse.  This approach allows you to start small and move fast. It provides the best of both worlds – flexibility and agility without breaking the bank.  You can RAPIDLY serve up quality data managed by your data warehouse, blended with newer data sources and data types in the cloud, and apply integrated analytics such as Spark or R – all without additional IT resources and expertise.  How is this possible?  IBM dashDB.

Read Aberdeen’s latest report on The Hybrid Data Warehouse.

mona's blog

 

Watch Aberdeen Group’s Webcast on The Hybrid Data Warehouse.

Let me give you an example.  We live in a digital world, with organizations now very interested in improving customer data capture across mobile, web, IoT, social media, and more for newer insights.  A telecommunications client was facing heavy competition and wanted to quickly deliver unique mobile services for an upcoming event in order to acquire new customers by collecting and analyzing mobile and social media data.  Taking a hybrid data warehouse approach, the client was able to start small and move fast, uncovering new mobile service options.

Customer information generated from these newer data sources were blended together with existing customer data managed in the data warehouse to deliver newer insights.  IBM dashDB provided a high performing, public cloud data warehouse service that was up and running in minutes.  Automatic transformation of unstructured geospatial data into structured data, in-memory columnar processing, in-database geospatial analytics, integration with Tableau, and pricing were some of the key reasons IBM dashDB was chosen.

This brings me back to my first point – you don’t have to spend millions of dollars to capitalize on getting data in and getting data out.  For example, clients like the one described above took advantage of Cloudant JSON document store integration, enabling them to rapidly get data into IBM dashDB with ease– no ETL processing required.  Automatic schema discovery loads and replicates unstructured JSON documents that capture IoT, Web and mobile-based data into a structured format.  Getting data or information out was simple, as IBM dashDB provides in-database analytics and the use of familiar, integrated SQL based tools such as Cognos, Watson Analytics, Tableau, and Microstrategy.  I can only conclude that IBM dashDB is a great example of how a highly compatible cloud database can extend or modernize your on-premises data warehouse into a hybrid one to meet time-sensitive business initiatives.

What exactly is a hybrid data warehouse?  A hybrid data warehouse introduces technologies that extend the traditional data warehouse to provide key functionality required to meet new combinations of data, analytics and location, while addressing the following IT challenges:

  • Deliver new analytic services and data sets to meet time-sensitive business initiatives
  • Manage escalating costs due to massive growth in new data sources, analytic capabilities, and users
  • Achieve data warehouse elasticity and agility for ALL business data

mona_dashDB

Still not convinced on the power of a hybrid data warehouse?  Hear what Aberdeen Group’s expert Michael Lock has to say in this 30 min webcast.

About Mona,

mona_headshot

Mona Patel is currently the Portfolio Marketing Manager for IBM dashDB, the future of data warehousing.  With over 20 years of analyzing data at The Department of Water and Power, Air Touch Communications, Oracle, and MicroStrategy, Mona decided to grow her career at IBM, a leader in data warehousing and analytics.  Mona received her Bachelor of Science degree in Electrical Engineering from UCLA.

Hybrid data warehouse architecture: many choices for full flexibility

by Matthias Funke

matthias blog pictureI admit, I love cars. And as a car enthusiast, I cannot imagine not having my own car. I use it every day, I rely on it. I feel at home when I enter it. But I accept that other people may be different. Some are not as attached, some can’t drive or don’t need a car often enough to justify the purchase. For them, using a cab or car service might be the better choice. And then there are people who need flexibility; they need a pick-up truck one day, a van the next day, and a sports car on the weekend. In short, they want full flexibility.

What does all of this have to do with IT and Data Warehousing? Well, at IBM, we think most of our clients have similar, diverse needs when it comes to their data warehouse environment. Depending on the use case at hand, one of several different data warehouse form factors may be better than the others for a particular analytics workload at that time.

Depending on the use case, one of several different data warehouse form factors may be better than the others for a particular analytics workload at that time.

Should it be hosted, vendor-managed, or do I want complete in-house control? Do I need full flexibility regarding the service levels I set for my clients, or is it sufficient to work within the distinct configurations that a service or vendor provides? Behind the scenes, all of this directly impacts the combination of compute versus storage resources I want to have to deliver the right level of  flexibility in the most cost-effective way.

No longer is data warehousing a one size fits all approach.  You need to weigh factors like the service level and the importance of meeting it, amount of flexibility you need, cost of the solution and the amount of control that each of your analytics workloads requires.  In line with this, we  see demand for three distinct data warehouse form factors:

  • Managed cloud service – A vendor-managed public cloud service is the most simple to use as it requires no system administrator on the client side. It is easiest to engage with because you can instantiate a service very quickly, paying for what you use at the moment (“pay-as-you-go”).
  • Predictable, high performance appliance – A client-managed data warehouse appliance offers the best predictability and performance due to its balanced, optimized software and system stack (including hardware), and the best price-performance when use cases require long-term, high utilization of a warehouse. Depending on client skills and effort, the appliance might offer the best simplicity and management, as well as lower TCO.
  •  Software-defined or private cloud software – A client-managed data warehouse service that would run on either your infrastructure, or a hosted IaaS (think Softlayer) is a third option. Use it when you want to increase utilization of existing infrastructure investments and when you need full long-term flexibility to adjust the service depending on the analytics use case and the LoB demand for the analytics. As I stated above, adjusting service levels means  you need the control and flexibility to adjust the combinations of compute  and storage resources to meet current needs. In this scenario, you have control and management of the infrastructure, and  you can enjoy appliance-like simplicity of the data warehouse while still being able to manage it yourself.

Now what if you want to use each of the above form factors in differing combinations to meet a variety of needs?  What if you could choose the best form factor for each workload at that moment in time? Integration across instances of each form factor could enable you to load or replicate data, or to abstract users and applications from the physical layout of your data stores. This becomes a critical success factor in building logical, hybrid data warehouse solutions that offer best flexibility and agility for the business at the lowest cost plus the ability to marry fit-for-purpose data stores, structured and unstructured, into the overall architecture.

No longer is data warehousing a one size fits all approach.  You need to weigh factors like the service level and the importance of meeting it, amount of flexibility you need, cost of the solution and the amount of control that each of your analytics workloads requires.

If you follow IBM, you know that we just launched a Preview Program for our IBM dashDB Local as a software-defined environment (SDE) data warehouse deployment option.  It addresses the needs of the software-defined / private cloud form factor above and it complements the IBM PureData System for Analytics appliance and the dashDB managed cloud datawarehouse service we already offer. Take this new preview for a test drive and tell us what you think so together we can shape the hybrid data warehouse architecture of the future.

About Matthias,

Matthias Funke_headshot Matthias is the worldwide leader of the IBM Data Warehouse product line and strategy. He is passionate about data as the “new currency” and looks for new ways to deliver insights from this data. Matthias brings many years of technology experience to his role including product management, software development and leading software development teams.

Follow Matthias on LinkedIn