Moving an existing data lake to a cloud environment or building a brand-new data lake on the cloud, requires a great deal of work. Understanding what potential obstacles can present themselves and how to overcome them will help ensure a smooth transition. The aim of this article is to shed some light on how to achieve this.
For many businesses, the biggest obstacle is not technical, but rather working through business-specific issues. Here are a few challenges that many CIOs or storage managers need to address to make this move:
Budget predictability – In almost all cases, the total costs of a cloud data lake will be lower than an in-house solution. The costs of a cloud solution, however, fluctuate based on per-hour billing, which can make it a difficult sell to the ultimate decision-makers. Our dedicated consultants can help simplify the complex costs for people at all levels.
Reorganizing talent – While executive level management often sees the value in reducing IT engineering costs, the idea of moving the management and support of a data lake to a third party can be difficult. This is especially true when seeking buy-in from team members across all levels. Based on our experience, most businesses do not have to lay off people after this type of transition. Instead, they are free to focus their talents on initiatives that directly support business objectives rather than just keeping the data lake operational.
Security – There is no doubt that a cloud data lake is more secure than virtually any in-house solution could possibly be. Due to high-profile data breaches, however, this can be a hard sell to some members of management. Our consultant can outline the modern security features that cloud data lakes provide in a way that both technical and non-technical resources will appreciate.
Technical challenges can be significant whenever a new cloud system is set up or an existing cloud system is transitioned. While data lakes are still a relatively new option for many businesses, the technology behind them is well-established. Some technology-focused issues that often need to be addressed include:
Transitioning existing data – You may have years’ worth of data stored in-house, either on an internal data lake or separately across multiple systems. Planning the best way to efficiently upload historical data to a cloud data link is something that our consultants specialize in. This can be done while also sending newly generated data to the cloud for production use immediately.
Internal analytics – Internal analytics tools often will not naturally transition to a cloud data lake environment. This is because the tools are either customized for an in-house data lake or more likely, separated based on siloed data storage environments. Fortunately, there are cloud-focused analytics and artificial intelligence tools available to better interact with data. Our consultant can provide any required training to ensure your users are able to take advantage of these tools.
Choosing a cloud host – We recommend using either Amazon Web Services (AWS) or Microsoft Azure – the two largest cloud platforms in the world, and with good reason. We are an AWS Service Advanced Partner as well as a Gold-tier Azure Managed Service Partner, so we can help create and manage an effective data lake on either of these popular options.
We have helped businesses around the world with cloud data lakes and have seen just about every challenge you can imagine. We have the experience and technical contacts with both AWS and Azure to successfully navigate any cloud transition.
Contact Us Today
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
A Winning Base for Successful Digital Transformations When it comes to developing a successful digital strategy, it is not just corporations planning to maximize the benefits of data assets and technology-focused initiatives. The Government of Western Australia recently unveiled four key priorities for digital reform in its new Digital Strategy for 2021-2025.Explore
Engage Your Workforce with a Modern Employee Intranet Solution The employee intranet has changed significantly since it was first introduced in the early 1990s. What started as HTML-based static portals have now evolved into intuitive communication tools complete with search engines, user profiles, blogs, event planners, and more. Today, many organizations are taking a second look at employee intranets to bridge gaps between teams, build company culture, centralize information, increase productivity, and improve workflow.Explore
Adopting emerging cloud technologies, consolidating resources, and improving processes is the key. “IT no longer just supports corporate operations as it traditionally has but is fully participating in business value delivery. Not only does this shift IT from a back-office role to the front of business, but it also changes the source of funding from an overhead expense that is maintained, monitored, and sometimes cut, to the thing that drives revenue,” said John-David Lovelock, research vice president at Gartner.Explore
Deliver Powerful Insights Instantaneously with Federated Queries - No Matter Where Your Data Resides The concept of federated queries isn’t new. Facebook PrestoDB popularized the idea of distributed structured query language (SQL) query engines in 2013. Over the years, AWS, Google, Microsoft, and many others in the industry have accelerated the adoption of a distributed query engine model within their products. For example, AWS developed Amazon Athena on top of the Presto code base, while Google’s BigQuery is based on Cloud SQL.Explore
What is Unstructured Data? Almost 80% of the data that enterprises and organizations collect is unstructured - data without a set record format or structure. Unstructured data includes data such as emails, web pages, PDFs, documents, customer feedback, in-app reviews, social media, video files, audio files, and images.Explore