A quick overview of the analytics lifecycle, the growing number of tools and technologies available, and how to choose the best data platform for your purposes.
The underlying infrastructure for applications, particularly for analytical workloads, is currently one of the most critical problems in the technology sector. Are the benefits of a single, tightly integrated public cloud stack worth the dangers of vendor lock-in? Is a multi-cloud strategy providing you with more bargaining leverage and complexity? Is it better to put apps in a private, on-premises data centre to secure your data? All of these are fair questions now, but they won’t be in the future.
Why It Won’t Matter Where Your Analytical Workloads Run Anymore
Over the last decade, public clouds have grown in popularity exponentially. It took a long time to persuade critics that security and scale could be addressed in business data centres as well. Despite this, investors continued to fund all of the public cloud companies in the hopes of spreading the word. When ISVs began to construct cloud-native applications, the industry exploded, leading investors and customers to pay attention to the many diverse applications available.
The public cloud providers found themselves on the verge of defining a commodity computing and storage technology that was nearly identical, prompting them to expand into application development rapidly. This was especially true for analytics and data warehousing applications, given the enormous datasets. A succession of acquisitions was made in the industry to extend into platform-based products, such as BI visualisation tools, streaming and edge analytics, and so much more, paving the way for AWS, Google, Azure, and others to achieve record growth.
SaaS, microservices, containerisation, Kubernetes and DataOps developments accelerated the next wave of transformation. The next generation of analytical workloads will “plugin” to whatever compute/storage grid makes the most sense at the time, regardless of location, cost, laws, or other factors.
The Consequences of Not Caring About Where Your Workload Is Distributed
In the end, an automatic choice of optimum infrastructure will maximise cost savings. Imagine a future where you can analyse and select the most reliable, highly scalable advanced analytics and machine learning platform without worrying whether it will be supported by a public cloud or an on-premises private data centre. When this is combined with automated administration, elastic scalability, auto-scheduling, and a fully integrated set of analytics and machine learning, it will be on a far faster track to data-driven business insights and proactive analytics-driven actions.
Data democratisation is a key business motivator for this analytics-anywhere strategy. Assume your company’s objectives for supplying analytics include limitless concurrency and quick response times. You want to be able to deliver analytics to anybody at any time. In such instances, the shift will necessitate software elasticity and performance optimisation, which is presently not a key priority for businesses that profit from computing hours. Too many suppliers see data democratisation as adding additional cloud servers with no opportunity to improve and prioritise queries. There must be a delicate balance.
In many companies, there is no defined structure for selecting databases. Business analysts frequently choose which solutions they want to use, and as usage grows, so does adoption. When data scientists and business analysts join a firm, they bring knowledge of languages, visualisation tools, analytics engines, and preferences for the tools they are familiar with. Other critical considerations such as data governance, operational efficiency, and cost management are often considered equal with deployment simplicity by smart IT and data governance teams.
It’s critical to examine the six areas of how a cloud platform should act when you assess solutions for their capabilities and long-term cost to the organisation:
- Deployment Options: Will this cloud database’s deployment choices enable all of the workloads you wish to run today and in the future? SaaS, hybrid cloud, on-premises, and microservices are examples of this.
- Storage And Access To Data: Will the database allow hybrid data storage models, in which data is stored in a cloud object store, on-premises object store, or any of the hundreds of additional locations? Will it access and analyse the data without putting a heavy burden on the system?
- Optimisation And Speed: Will the database be able to manage all of your data today and in three years? Is it adaptable and scalable? Is it capable of loading data quickly enough and serving many concurrent queries? Is it possible to modify slow-running questions, or is adding expensive computation the only option?
- Analytics: Is the solution equipped with the level of analytics you want, including time-series, geospatial, machine learning, and other features? Is it possible for Python users to access the same data as SQL users?
- No Invoice Surprises: Is it possible to set explicit spending limitations with the solution, or will you be surprised at the end of the month?
- Secure: Is it a safe environment to protect your sensitive information with ISO 27001 accreditation and encryption?
Only focusing on what’s essential will you come away with an analytical solution that is viable for decades to come.
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