Managing your own open source analytics services creates a host of headaches
and challenges. In the past, businesses with large resources and a highly
specialized IT staff could minimize the negative business impacts of these
difficulties.
But over the last several years, multiple factors have turned the prospect
of self-managing an efficient and effective analytics environment into an
impractical scenario for businesses of every size. Data volumes are exploding,
data must be ingested from a growing list of computing and IoT devices, and
new types of data are emerging faster than most businesses can manage on
their own.
To fully understand and appreciate the adverse outcomes of self-managed
analytics, let's break the list of associated challenges into categories and
explore them individually:
4
CHALLENGES
Performance concerns
Many businesses are experiencing growing pains around
performance with their existing legacy and self-managed
analytics services. Performance in these environments is
not agile nor quickly adaptable to changing data volumes
and workloads. As more data is stored and processed,
performance tends to decline until more resources are
deployed and implemented.
Lack of scalability
Today's businesses need the flexibility to scale storage
and compute independently. They also need the ability
to quickly scale compute up or down based on business
needs. With traditional, on-premises environments,
growth often means procuring new hardware and
software, necessitating many hours of highly skilled,
manual effort.
The struggles of self-management
