Analytics Data Management in the Cloud book cover

Analytics Data Management in the Cloud

There are many tools on the market that help with pieces of an analytics architecture, including cloud providers. Let’s sort through those options and explore ways to simplify your architecture to make it more agile and deliver analytics faster and easier.

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About The Analytics Data Management in the Cloud Book

Today’s leading cloud platforms include numerous components for storing, processing, and analyzing large volumes of data. All the basics are there: storage, analysis, and processing, streaming data processing, data pipelining, data warehousing, BI, and even AI. But while it’s great to have all those raw components, how do you tie them together and build a true data pipelining and analysis solution? While the parts are great, they still must be assembled into the whole

This paper will explore the various services available to you on the Amazon Web Services platform. We’ll discuss the powerful solutions possible with each of them and get a sense of how they can be combined. Then we’ll review various options for tying them together, including methods somewhat bespoke and using products that sit atop these components, integrating them seamlessly in the background.

The cloud provides a platform — it doesn’t always provide a full solution. Our goal in this paper is to set you on the right path to obtain a solution that mitigates risk and frustration and safeguards project success.

Why the Cloud?

Let’s explore the many benefits of using the cloud for analytics and how it can help modernize your architecture.

Components in a Cloud Analytics Architecture

See some of the many components and options in cloud data and analytics architecture and how this can lead to unwanted complexity.

Why Buying is Better than Building

We’ll explain why buying commercial products covering a range of functionality in the analytics stack is better than stitching together multiple micro-tools.

DataOps Process: How it helps

How Datameer Spectrum Helps

Learn how Datameer Spectrum can fit into your cloud data and analytics architecture to streamline the process of migrating workloads to the cloud.


A number of industry trends have combined to create the market demand we see today for public cloud solutions. Among them:

  • Paying for what you need: the cloud works on a combination of elastic resource deployment and utility-based pricing. Rather than having to lay out significant capital funds to acquire technology infrastructure for your heaviest intermittent workloads, the cloud lets you use operating expense funds to pay for just the resources you need. This applies to both computing and storage resources.

  • Externally-borne data: an increasing amount of enterprise customers’ data originates off-premises. This means the data needs to be collected and consolidated into a single location and one that needn’t necessarily be on-premises. By extension, the analytics infrastructure and software that will process this data needn’t necessarily run on-premises, either. Cloud storage can be an ideal place to land the data, and cloud platforms may be the best place to run the processing and analytics on that data.

  • Rapid obsolescence cycles: innovation in hardware infrastructure, whether it be around storage, memory, or processing (CPU or GPU), proceeds at a rapid pace. Hardware purchased now will obsolesce in less than a year. This makes ownership and physical installation of such infrastructure by the customer less attractive. As rapid upgrades are desirable — and sometimes necessary for competitive reasons — renting (in the cloud) is better than owning (on-premises).


When it comes to analytics, cloud providers seek to supply you with many core components, each extremely able and feature-rich in its own domain. Stitching these components together and providing higher-level abstractions that subsume multiple components is not. However, something cloud providers specialize in.

For the most part, this lack of turn-key solutions over multiple services is by design; cloud providers tending to leave such value-added solutions to partners, while the providers themselves focus on the core platform. This is not so different from how many enterprise software companies built their own platforms, leaving room for Software Integrators (SIs), both large and small, to deliver integration and customization services at the top of the stack.

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