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Moving Big Data into the Cloud

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We’ve all seen statistics demonstrating the amount of data being generated and gathered daily with average amounts in the Petabyte and Exabyte range. Processing such large amounts of data is what big data is built to do. It’s no wonder that the big data industry has grown so large with the amount of data and the potential business surrounding it.

While there are multiple architectures targeting big data analytics, we have traditionally focused on maximizing dedicated hardware and large processing power. In the past few years, however, there is a fast growing convergence of big data and cloud, particularly where the data sets are unstructured with simple data models — an area of specific focus for the Apache Hadoop technology.

Where big data has traditionally opted for dedicated hardware, what is now driving it to the cloud, and what are the benefits? Cloud computing espouses dynamic workloads, multi-tenancy and the sharing of resources. The clue is in the growth and expansion of analytics ideas and data based solutions, for example the expansion into real time on-demand analysis and response. A great example of this would be the consumer shopping experience demonstrating need for real-time, on-demand analysis.

Providing real-time services is the perfect fit for cloud, including real-time analytics needed for quick elasticity, rapid service deployment and agility. As businesses discover the potential of an enhanced customer relationship, ideas and innovative methods for that relationship drive business growth. Yet the services for this growth profile are different than traditional analytics. Real-time analysis and storage vary over time and location. In other words, these types of services need to be elastic to rapidly respond to changing workload demands over time.

Certainly, there are challenges with cloud adoption. Security concerns and the mere fact that migrating big data to the cloud requires a lot of effort, offer pause to companies considering cloud migration. Oftentimes, a hybrid cloud solution is considered the most modern data architecture – where you’re able to spin up separate storage and compute instances to accommodate when they’re needed and take them down when they’re not. This demand-based efficiency represents a massive cost savings.

Most enterprises that scale out big data to the cloud run into the same challenges of managing ever-increasing complexity and ensuring governance, resulting in wasted spending, increased risk, and agility loss. There’s a need to collapse the complexity of managing the cloud at scale and enforcing compliance and security policies at every step of cloud operations. Enterprises shouldn’t be forced to make trade-offs between control, speed, and spend.

An enterprise should centralize the management of multiple, dynamic cloud environments across many cloud accounts (e.g. Amazon Web Services, Google Cloud, Azure, etc.), bringing control and visibility to cloud usage and operations. Cloud resources should be run as managed processes, which provides a concise, real-time view of cloud resource topology. This allows the enterprise to avoid inefficiencies such as orphaned infrastructure that can lead to wasteful spending, security vulnerabilities, and time-consuming audits. There needs to be an operational source of truth and trust for the cloud.

Moving big data into the cloud requires a comprehensive solution that compiles, configures, manages, automates, monitors, and enforces cloud workloads, helping infrastructure & operations teams quickly and proactively optimize cloud operations to ensure an outstanding end user experience. Here is a short list of considerations:

  • Time to value for the business – Enterprise should strive to ensure that the time required to to deliver value to the business is much shorter.
  • Mitigate Risk – Enterprises should strive to employ enhanced role-based access controls to regulate the creation and modification of cloud resources so teams can interface securely. Compliance and security rules can be implemented as code (policy-as-code), ensuring that infrastructure changes comply with organizational policies and practices before changes are applied. Change plans can be included in approval processes, and all changes are logged for auditing purposes.
  • Eliminate Configuration Drift – Enterprises should continuously manage and enforce desired infrastructure state so teams can collaborate on running infrastructure safely without incurring the burdens of managing state locally and the associated risks of deployment failures and application downtimes.
  • Enforce Consistency – Cloud architects and DevOps teams should work to simplify and share infrastructure compositions, pattern libraries and best practices to ensure consistency across workloads and significantly reduce the time to configure infrastructure.
  • Quick Access to Dev and Test Environments – DevOps teams and Managed Service Providers (MSPs) should be able to easily spin up and tear down development, test and staging environments. Making environments quickly available to the right teams eliminates typical wait times that impede IT agility.

Today’s businesses maintain a need for technical efficiency to control cost, risk and security. Big data in the cloud has proven to be the most viable answer, providing businesses the agility and elasticity they are looking for while also providing centralized control and stability.

About the Author

This article was written by the staff of Clarity Insights, a big data and data science consultancy. It is the largest 100% onshore big data and data science consultancy in the United States. Clarity Insights helps companies unleash their insights by creating data strategy, building data platforms, and finding actionable insights that build processes & culture.

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