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3. Storage
On-premises storage can be costly and complex, with expensive hardware refresh cycles and data migrations required
to support system upgrades. In addition, gaining insights from data is difficult if data is trapped in silos or other "muddy
pools" across the organization.
4. Analytics systems
Managing open-source analytics software like Apache Hadoop/Spark, Elasticsearch, and Apache Kafka on-premises is
complex, time-consuming, and expensive. Challenges to this approach include keeping a dedicated team of experts to
manage hardware and software configuration, patching and backups, tuning and optimizations for performance, and
capacity planning for future growth. A move to managed analytics in the cloud can save time, reduce costs, and signifi-
cantly improve productivity.
Moving storage workloads from on-premises systems
to the cloud can reduce total cost of ownership through
a flexible buying model that helps to eliminate over-pro-
visioning, shorten refresh life cycles, and reduce the cost
of maintaining storage infrastructure.
Moving storage to a cloud consumption model lets
companies adjust on the fly and use whatever storage
they need now—without being locked into a hardware
refresh. It can also keep organizations agile, reduce costs,
and provide for unlimited scalability while also eliminat-
ing data silos.
Key components of a modern data infrastructure
MODERNIZING YOUR DATA INFRASTRUCTURE
