Six Key Reasons to Switch to a Cloud-Based Data Warehouse

Data warehouses, which aggregate data from multiple sources so it can be analyzed and acted upon, are critical for business growth. Their ability to quickly process the massive volumes of data today’s businesses are collecting allow leaders to confidently implement data-based decisions.

However, almost two-thirds of professionals recently surveyed describe the management of their data warehouse solution as ‘difficult’ or ‘very difficult.’

As the complexity and volume of data continues to evolve, cloud-based data warehouses are emerging as the most efficient way to reduce that complexity while maintaining the agility, security and performance data analysts increasingly expect. Businesses are moving to cloud-based data warehouses for the following reasons:

To cope with big data. Cloud-based data warehouses provide the flexibility to grow storage independently of compute resources. This allows for the ingestion of large volumes of data without the associated compute costs. Switching to a cloud-based data warehouse also enables organizations to scale in a matter of minutes, or even seconds, based on fluctuations in data growth. Controls are easily accessible via APIs and dashboards, and there is no need for organizations to pre-plan, prepare advance procurements, or worry about running out of space.

To support multiple data formats. Because data may lack structure, cloud-based data warehouses support various semi-structured data types like JSON, CSV, and a bevy of other formats. Some services also support the querying of un-structured data on external storage. By supporting multiple query and data formats, cloud-based data warehouses can handle semi-structured and un-structured data typical of big data workloads. The added advantage for big data workloads is that most cloud-based solutions provide longer term cold storage options where data is easily accessible, but at a lower storage cost and with minimized business impact.

To accommodate end users. A cloud-based data warehouse provides support for ad-hoc and parallel queries on the same data set without penalties on the performance of existing workloads, allowing end users to explore the data with very little limitations. Additionally, simplified data ingestion mechanisms and ELT processing reduce the burden on users maintaining complex ETL pipelines. Users can concurrently run many queries across terabytes of data and get responses in as little as a few seconds.

To reduce TCO. Cloud-based services provide pay-per-use billing, which is the same economic model used for data warehouse solutions. Most of the cloud-based data warehouses separate storage and compute for performance and scalability requirements. The cloud cost structure allows organizations to pay for storage and compute both separately or bundled, but still based on usage levels for each. With cloud-based data warehouses, businesses can reduce their hardware costs by preventing hardware expansion and reduce their maintenance burdens by nearly eliminating costs for associated personnel, licenses, and hardware replacements.

To improve security. Cloud-based data warehouses use hardware accelerated AES-128 or higher encryption for data at rest. All data in transit between compute resources, across regions, and between services are TLS encrypted. Most cloud-based data warehouses include support for virtual private networks with connectivity to on-premise networks utilizing industry-standard IPsec VPNs. Most organizations would need devoted in-house teams to manage this level of security infrastructure. Even with an in-house team, in most cases, organizations could not match the level of sophistication and ease of applying the security controls in a cloud environment.

To improve disaster recovery and business continuity. The majority of cloud-based data warehouses split storage and computing to support asynchronous replication of the storage across regions, without impacting the existing compute resources and queries. Backups and snapshots are still taken automatically and made available within the given region. Some vendors offer their own private backend networks between regions to further reduce latency, increase reliability and security, and improve availability with little to no data lost during the recovery process. In disaster scenarios, the cross-region replicated data can be immediately put to work by spinning up processing capacity where availability ranges from immediate to a few minutes of ramp-up time. Certain systems support immediate querying of a portion of the data while the rest of the data is loaded seamlessly in the background. These features are unmatched with traditional on-premise data warehouses.

Though cloud data warehouses present many benefits that help organizations run more efficiently and free in-house teams from maintenance and operations burdens, they can be complex to stand up and tune to the specific needs of the business. And the benefits of cloud-based data warehouses don’t come free — businesses will still have to architect cloud-based data warehouses and understand new paradigms and pricing models. Rackspace’s Professional Services team can help businesses implement the transition, offering unbiased expertise and dedicated support.

[Read more: How (the Right Kind of) Professional Services Can Boost Your Business]

Our experts can also help businesses plan, deploy, migrate and manage your cloud data warehouse, ensuring you obtain the most value from your digital transformation. Learn more about Rackspace data management services.

Nirmal is a Principal Architect at Rackspace, leading Data Transformation services as part of Rackspace’s Professional Services organization. Nirmal consults with clients around large scale databases and data processing, data analytics and data warehousing in the cloud, plus machine learning and artificial intelligence, providing recommendations and solutions for a wide variety of industry verticals. Nirmal has a strong background in cloud and distributed systems, having contributed to various open source projects from Cassandra to OpenStack. Prior to his consulting role, he was a lead engineer on Rackspace’s Cloud Databases and Cloud Big Data Product Engineering teams.


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