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The term data lake has been on the rise and is being used more frequently, but it’s important not to confuse this with data warehouses. While both are used for storing data, they are not interchangeable terms. Many view data lakes as more beneficial for their company- in fact, 43% of businesses reported that they found data lakes increased their operational efficiency.
The terms data lake and data warehouse are often used interchangeably. However, data in a data warehouse has been cleaned and structured for a specific use, where data in a data lake has not. The two types of data storage are often confused, but are much more different than they are alike. In fact, the only real similarity between them is their high-level purpose of storing data.
The distinction between the two is important because they serve different purposes and require different sets of eyes to be properly optimized. While a data lake works for one company, a data warehouse will be a better fit for another. The one undeniable fact is that organizations who successfully generate business value from their data will outperform their peers.
The amount of data being gathered is growing exponentially. With the advancements in compute power, businesses can afford to leverage this data to drive decision making. Data lakes provide a cost-effective way to store years of data while maintaining data integrity so that it can be used in the future.
Using data lakes allows for new types of analytics such as machine learning over new sources like log files, data from click-streams, social media, and internet connected devices. This helps in identifying opportunities for business growth at a much faster rate through attracting and retaining customers, boosting productivity, and making informed decisions.
“The true value of a data lake is realized when it’s used as a source for business intelligence and analytics.” Brianna Buttaccio, Cloud Engineer at EagleDream Technologies. “We build reporting dashboards for customers that combine data from sources like SalesForce, CostGuard, internal product databases, and website forms. We also develop machine learning models to forecast key business metrics.”
Let’s face the fact, data lakes are the ideal workload to be deployed in the cloud, because the cloud provides performance, scalability, reliability, availability, a diverse set of analytic engines, and massive economies of scale. Our customers are taking the leap to data lakes for the increased security, faster time to deployment, better availability, more elasticity, and more geographic coverage to name a few. Contact us today to learn how you could be taking advantage of these benefits.
Andrew leverages over 15 years of development experience specializing in enterprise applications and integrations. He has unparalleled expertise in full software life cycle delivery with an eye toward Agile development methodologies. Among Andrew’s development experience, he excels in databases, APIs, system integrations, and mobile applications. Andrew maximizes client and team efficiency with success as a technical lead and has rich experience in project leadership and management.
Prior to joining EagleDream, Andrew worked at Sharp Notions, and before that at PAETEC where he led the team responsible for delivering a CRM and Customer Service platform that integrated with downstream provisioning and billing systems to provide a complete, real-time 360-degree view of a customer. Andrew was also involved in delivering complex reporting portals, business workflow engines, and sales quoting tools to help provide an efficient, stream-lined and integrated telecom application environment. Learn more about EagleDream’s Application Development practice here.
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