As the saying goes, big data is what keeps the world turning. The digital economy runs on targeted advertising, consumer behavior research, and business intelligence. Put simply, businesses that use data and analytics to their advantage see a dramatic increase in revenue.
To make the most of the data at your disposal, you’ll need to create a unique platform for your business’s needs. The three most popular data storage patterns are data warehouses, data lakes, and lakehouses. To build a reliable data storage pipeline, it is crucial to have a firm grasp of the various big data storage techniques available today.
Data Warehouse vs Data Lake vs Data LakehouseÂ
What is Data Warehouse?
A central database processes all queries in a data warehouse. Most data warehouses keep active and archived data, expanding the scope of available reports and analyses. Everything that comes in, whether financial data, sales figures or input from end users, is stored in one centralized location. Relational tables are commonly used in data warehousing to construct profiles and analysis measures. Here, you can know more about bespoke business intelligence services which include all the terms.
Benefits
There can be a great deal of data uniformity across an organization when using a data warehouse because of the central location of its storage facilities. Data warehouses facilitate enhanced decision-making as a direct result of the increased reporting and analytics capabilities afforded by the framework.
Drawbacks
Data warehouses are effective tools, but building your system can be expensive. However, modern systems make it possible for businesses of any size to plan and build their data warehouse rapidly and cheaply. Data warehouses excel in storing and organizing structured data but need help with unstructured information generated by log analytics, streaming, and social media. Therefore, it is not a good fit for businesses that want to implement machine learning initiatives.
What is Data Lake?
Data lakes can store both organized and unstructured information in their raw formats. Extracting, loading, and transforming data is a common workflow in most systems. Because the schema is undetermined at the time of capture, there is no need to cleanse data before sending it to a data lake.
Benefits
Data Lake architecture is less complicated to set up and less expensive than data warehouses. Because of this, they’re also much cheaper than comparable alternatives. Data lakes offer greater versatility regarding the types and formats of data they may store. And they can take on machine learning and predictive analytics projects with ease.
Drawbacks
Data lakes aren’t ideal if your company’s model necessitates very accurate metrics and KPIs. Also, hiring employees to run a data lake pipeline is very expensive. Although data lakes have the potential to be extremely helpful, they need close oversight to be properly utilized. Due to their decentralized design, data lakes often contain overlapping information.
What is Data Lakehouse?
Data lakehouses combine aspects of both data warehouses and lakes. The same is true for the lakehouse; everything is stored in a central location. They, too, allow for predictive analytics and machine learning on a wide variety of data types, including unstructured varieties.
Benefits
Data lakehouses are more cost-effective than traditional data warehouses because they use object-based cloud data storage strategies. Since all lakehouses can access the same database, the maintenance required to keep them running smoothly is reduced. Data lakehouses have superior security because they are far less vulnerable to common threats that plague data lakes.
Drawbacks
Problems may occur after deploying a data lakehouse. There could be lingering flaws in the depths of the code. This is why it’s smart to set aside money in emergencies.
How do you Choose Between a Data Lake, a Data Warehouse, and Data Lakehouse?
A data lakehouse can be difficult to construct from scratch. You’ll also want to use a platform that was developed to work with open-data lakehouse designs.
If you’re looking for a reliable, mature, structured data solution tailored specifically to your business’s needs for BI and analytics, go no further than a data warehouse. However, data lakes are well-suited to businesses that need a scalable, low-cost big-data solution to power machine learning and data science workloads on unstructured data.
Consider that your business needs more than what can be provided by a data warehouse or data lake or that you want to leverage your data for advanced analytics and machine learning but don’t know how. A data lakehouse is a practical option to consider, in this situation contact us at [Protected Email] to know more about these services.
