Managing large data sets to gain essential insights and create better business decisions drives companies to invest in concepts like a data lake and a data warehouse.
Centralised data storage systems have large capacities and meet the specific needs of organisations.
We are currently creating and consuming close to 100 zettabytes of data, a volume that is hard to wrap your head around.
While some worry about where to store such excessive amounts of data created in a digitalised world, businesses can thrive using gathered data for data-driven decisions.
A data lake is a type of centralised storage that allows you to store all kinds of data. Unlike data warehouse and other standard data storage options, data lake accepts both structured and unstructured data.
The data lake concept is unique because it stores data in its raw format, preserving all of the original content, logs, and formats. A data lake stores relational data from business applications and non-relational data from sources like IoT devices, social media, mobile apps, and others.
There is no preferred structure or format for data. Therefore, you will need data experts to extract insights from the data lake.
According to an Aberdeen survey, organisations that use data lake have 9% better organic revenue growth, outperforming similar competitors.
You must construct custom queries and apps to extract meaningful analysis from a massive lake of unstructured raw data. Unlike a data warehouse where data is cleansed through transformation scripts that remove excessive inputs before data is stored, a data lake preserves all potentially important details you can later use for analysis.
With a data lake, you can have classic queries like SQL for the structured part and more unique analyses for varied unstructured data.
A data lake can handle massive real-time data imports from multiple sources. As a result, you will save time on defining data structures and transformations, and an additional benefit is the ability to scale to data of any size.
A typical data warehouse is also a valuable tool for business analytics. Unlike data lake, it uses structured and formatted data.
You can quickly get insights and predictions from data gathered under strict harvesting. Many business applications can perform batch reporting, business insights, and visualisations.
Simultaneously, a data lake requires experts and advanced methods and frameworks like Apache Hadoop, Presto, machine learning, and predictive analytics. However, raw, unstructured, and democratic data gives more opportunities for critical exploits.
More data coming from numerous sources in less time can lead to more accurate predictions and other types of analysis. For example, you can combine different sources like CRM, social media, and marketing platform data to create more accurate user profiles.
You can test new theories or hypotheses to improve R&D or increase the effectiveness of your large-scale IoT systems where data analysis can spot pain points, reduce costs and increase quality.
Hosting a data lake in a cloud provider like AWS is the current trend for enterprises because renting capacity is more cost-effective than building infrastructure. You can also quickly scale the volume of your data lake.