Why and how to seize the multi-cloud data analytics opportunity?
Most businesses across industries exist in a world in which cloud infrastructure is the norm. The cloud is no longer an esoteric buzzword-laden concept we’ve decided to evaluate. Instead, cloud computing accounts for at least a part of most organizations’ architectures, and complete or hybrid cloud strategies have continued to become dominant. We’ve moved beyond the question: “should we move to the cloud?” into the “how can we leverage multiple cloud services to gain competitive advantage and differentiating offerings?”.
The multi-cloud moment
According to Flexera’s 2019 State of the Cloud survey, 84% of enterprises now have a multi-cloud strategy – a sign of how many companies have adopted the multi-cloud approach. Gartner seconds this view, citing similar statistics – 81% – about organizations that are working with two or more public cloud providers. As the competitive landscape has shifted, with Google Cloud Platform (GCP) and Microsoft Azure going head-to-head with industry leader Amazon Web Services (AWS) in the cloud space, enterprises have begun to look beyond one provider to gauge multi-cloud strategies as a way to build their own “best-in-breed” solution that meets their individual IT requirements and goals.
Aside from expanding on the flexibility and elasticity of the cloud model more generally, one of the drivers for multi-cloud deployment is its ability to empower organizations to take control of their enterprise architecture and data. The choice offered by multi-cloud service providers across different vendors unlocks possibilities in the data analytics space for managing data warehousing, business intelligence and analytics in new ways, taking advantage of open APIs and standards, allowing the user to define what they need, i.e. using each cloud solution as a utility that fits a specific role without being locked into any single cloud.
Four primary reasons propel multi-cloud adoption:
- Avoiding vendor lock-in: A multi-cloud environment frees an organization from being locked into a single cloud provider’s infrastructure, add-on services and pricing model. The flexibility of running different cloud setups ensures easier migration from one provider to another because you can design your cloud architecture with portability in mind. Similarly, the elasticity of multi-cloud management gives a company the opportunity to shift spend between clouds and cloud vendors – to optimize costs, to take advantage of differentiated services or in response to differing service levels.
- Geography: Different providers may offer better service, fewer outages, better support, less latency depending on where in the world one provider is located geographically. Meanwhile, larger organizations have begun building their own content delivery networks (CDNs) to secure availability, reduce downtime and achieve performance gains by locating services closer to end-users. Cloud technology enables this kind of control and helps companies select and optimize points of presence strategically.
- Governance: Regulations, such as GDPR, influence how companies store and manage data. A multi-cloud architecture that includes hybrid cloud options, whether via private cloud or on-premise service, can ensure that enterprises govern data appropriately and in line with legal and regulatory requirements.
- Resilience: Multi-cloud deployment is disaster recovery and data protection bundled into one - as the expression goes, don’t put all your eggs in one basket.
Multi-cloud for data analytics white paper
Find out how to seize the multi-cloud data analytics opportunity
How to get started: Multi-cloud for best-in-class data tools
Multi-cloud has enabled cloud computing to go from “brute force” compute and storage to differentiated services, such as working in a more sophisticated way with data. The three main cloud providers offer extensive tooling options to help organizations move, manage and analyze their data depending on what the organization needs. And with multi-cloud solutions, companies can pick and choose from among the best tools the main cloud providers offer as well as the best independent and open source tools on the market.
The bottom line is: a multi-cloud strategy gives the freedom to get the best of all worlds in data warehousing, data discovery, data analytics and machine learning. Each function can run in a different cloud if that’s what suits a company’s needs. This approach enables experimentation, ease in shifting workloads from one cloud to another, and the ability to find the right fit and get the most value from the multi-cloud setup. That’s not to say that, despite the ease of moving across clouds, all of this is a walk in the park. Cross-cloud expertise is rare, and multi-cloud mixing has its complexities; the human resource demand, strategic planning and hands-on expertise will go hand-in-hand in making a multi-cloud deployment successful.
Snowplow Insights and multi-cloud
Understanding these complexities, Snowplow wants to make multi-cloud a whole lot easier for data analytics by giving users the flexibility to work with their data across multiple clouds. Whether you have separate teams that want to access Snowplow data in different services across different clouds, have data or applications running in a different cloud from where you have your Snowplow pipeline set up, or want to migrate to another cloud and need to feed the Snowplow data to your other data sets, Snowplow supports your ability to pipe your data seamlessly across clouds in real time and supports:
- Real-time processing in each environment, streaming the data onto Kinesis in AWS, Pub/Sub in GCP and EventHub in Azure, to enable real-time and batch-based computation on the Snowplow data.
- Streaming the data into multiple, cloud-specific storage targets on each cloud, enabling our customers to take advantage of Redshift, Snowflake DB, Elastic and BigQuery data warehousing technologies.
- Load the data into AWS S3 and GCP Cloud Storage as data lake solutions.
We’ve put together a white paper to help you dive into the multi-cloud opportunity for data analytics and start thinking about your own approach to multi-cloud. Multi-cloud can be of great value not just to your data teams but company-wide.