From: JTP PR on
With Gartner's Business Intelligence group listing In-Memory analytics
at the top of their recommended Business Intelligence requirements for
enterprise; No wonder In-memory has become a trending topic in BI and
a new addition to executive shopping lists.

We thought we’d share with you our top 10 technical requirements, you
should be asking from an in-memory analytics vendor:

[10] Be open - allow other applications to connect to it (other than
the in-memory database provider)

It’s important to source a non-proprietary database, in-memory
databases are usually bundled with a visualization tool and it’s
important that the provider allows other tools to connect to the
database so that you can maximize your investment in the technology,
rather than being tied to a proprietary solution.

[9] Minimal administration overhead

In-memory analytic tools often introduce some of the same concerns
that OLAP stores create: namely, they usually create another data
source, with its own calculations and business definitions. This is
where tools such as Yellowfin differ from other in-memory approaches:
existing queries, reports and dashboards automatically take advantage
of an in-memory database, seamless to users. Administrators are not
adding calculations and business logic within another layer; they
reside within the existing meta-data layer for reporting that is
already built.

[8] Enterprise scalability

It is critical to select enterprise class infrastructure that enables
you to scale your deployment as your users grow.
All BI solutions must include enterprise administrative features, such
as usage monitoring, single sign-on and change management; and this is
just as true for in-memory solutions. It is therefore, critical that
you choose solutions that can provide enterprise class infrastructure
that enable you to scale your deployment as your users grow.

[7] Platform independent

It’s important when selecting an in-memory database that it be
platform independent. It should run on any hardware platform (PC, Mac,
SunSparc, etc.) or software platform (Linux, MacOS, Unix, Windows,

[6] Data Serialized to disk to enable rapid recovery

An image of the in-memory database is saved to disk, allowing the
system to quickly reload the image should the system need to be
restarted. This means that data can be loaded into your in-memory
database without the need to place additional strain on your
production or transactional servers.

[5] Integration with your existing data warehouse and OLAP cubes

While some vendors tout in-memory as a way of avoiding building a data
warehouse, this option usually applies to smaller organizations that
may only have a single source system. For larger companies that have
multiple source systems, the data warehouse continues to be the ideal
place to transform, model and cleanse the data for analysis.

Look for tools that are designed to integrate with and leverage
existing BI environments. An in-memory solution that is tightly
integrated into the visualization tool is critical. However, it is
equally important that the visualization tool can also access your
OLAP cubes and data warehouse tables without the need for an in-memory
middle-layer. Without this option a purely stand-alone in-memory
solution can lead to yet another version of the truth, adding
complexity to your BI environment.

[4] Web-based GUI development and deployment.

Some in-memory tools are not nearly as web enabled as their
conventional BI counterparts. This seems to reflect both technology
immaturity and a tendency to be a niche deployment. However, for
successful adoption with minimal administrative overhead web based
development and deployment is critical. Both the visualization tool
and in-memory database need to be server based deployments to ensure
that data access security and application upgrades can be easily
managed. Solutions such as Yellowfin provide a single web based
platform for delivering your Business Intelligence needs. From
connection through to design, modeling and visualization, your users
work within a fully integrated browser application that encourages
collaboration and an iterative approach to report development -
leading to analytical applications that meet the needs of your end

[3] Server side rather than Client side

Consider a scenario where users are able to conduct complex queries by
downloading up to 100 million rows of data to their desktop from many
data sources, or data feeds from the web. Sure the information can
then be sliced and diced into reports or users can create BI
applications on their desktops and share them with colleagues. This
sounds great in theory but is fraught with danger in practice. Not
only does this have a massive potential to create data silos but with
this level of data stored on a laptop, it is free to leave your
premises and get lost or stolen in the worst case or published without
any form of governance at best.

[2] Ensure real time data refresh - incrementally loaded

Because reporting data is extracted from a source system or a data
warehouse and then loaded into memory, data latency can be a concern.
Front-line workers in a customer service centre, for example, need
near-real-time highly granular (detailed) data. If an in-memory tool
contains last week’s product inventory data, it’s probably not of use
to customer service reps. Thus, the suitability of an in-memory tool
and the success of the deployment may hinge on the degree to which the
solution can automate scheduled incremental data loads.

[1] Data security must be of paramount concern

In memory applications have the potential to expose significantly more
data to end-users then ever before. This raises security issues
regarding how data is accessed, where it is stored and who has access
to that data.

In determining the best strategy for your in-memory deployment,
security needs to be foremost in your selection criteria. There are
two aspects of security. Firstly, where is your data stored and is
that storage secure? And secondly, who has access to that data store?
Transactional applications go to great lengths to embed data security
and access rules – ensure your in-memory database inherits these and
is not simply a data access free for all.

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