In terms of storage and data organization, data lakes and data warehouses don’t have anything to prove anymore. One is focused on storing data in its original form (whatever the format), while the other is focused on the analysis of structured data. Both are very popular with companies, but they need careful implementation and management. Here are a few tips on how to optimize data lakes and data warehouses.
How to optimize a data lake
-
Identify and define the company’s goal regarding data
It can seem basic, but it is important for companies to know which information they want to collect and what they want to do with it.
-
Plan/Organize the data ingestion
According to Analytics Insight, data ingestion is a key step, because if the data isn’t properly stored, it can be hard to access it later. Moreover, good data ingestion can help optimize future analysis and ensure good treatment of data streams. Good ingestion is done, among other things, with compacted data in readable file formats.
-
Make copies of the data
Companies should take advantage of the storage capacities of data lakes and store both the original and treated versions of the data. Storing data in its original form is useful when you need to review a past state of affairs.
-
Set up a retention policy
Being able to store more data for longer doesn’t mean having to store data forever. It’s important to have a retention policy to know which data must be erased, which data must be kept and where to find the data you choose to keep.
-
Understand the ingested data
Having visibility on the ingested data, which includes knowing the schema and the metadata, allows for the generation of ETL pipelines based on the most precise available data.
-
Partition the data
Data is generally partitioned by date, but companies can choose another criteria, like a country or a user. That limits the amount of data that has to be scanned during the request, thus optimizing the latter.
-
Data governance and access control
Governance tools make it possible to control who accesses which data, and that make data lake management easier.
-
Choose readable file formats
Using readable open-source file formats eases access to data via multiple analytics services.
-
Combine small files
Data streams produce small files every day, and that can impact the data lake’s performance on the long run. Compacting these small files can solve the problem.
-
Use AI and automation
Given the diversity and the speed at which data enters a data lake, it is important to automate the data acquisition and transformation processes. Companies can also use artificial intelligence and machine learning to classify and analyze data more rapidly and with precision.
If the big stake with data lakes is the optimization of data streams, the key one with data warehouses appears to be in its construction.
How to optimize a data warehouse
-
Work with internal and external experts
Forbes explains that a team must include internal IT experts, because they know the company’s needs, they can identify the right workflows and the right data model.
-
Make sure the data is clean
The information must be free of human error or outlier and must also be complete to allow for a relevant analysis.
-
Ensure an access to data
It is crucial to have an ETL process automating the access to operational data and its ingestion in the data warehouse to avoid a paralysis of the system.
-
Combine a data lake and a data warehouse for more flexibility
Being able to store data in its original form in a data lake upstream of a data warehouse make it possible to historize unstructured data.
-
Think about serving multiple departments of the company
Structuring the data warehouse so it can serve multiple departments reduces data silos.
-
Create an intuitive model for users
The data must be organized in an intuitive model that users can understand.
-
Make sure the data warehouse is flexible
The data warehouse must have enough memory and resources to adapt to the company’s evolution.
-
Have a backup
The company must be able to operate and grow, even when the data warehouse is down.
-
Think about security
As information repositories, data warehouses make quite the target for hackers. Data can be secured with strong cybersecurity foundations and network security testing.
Data lakes and data warehouses are two different technologies that aren’t exclusive. It’s pointless to oppose them, and many companies use both to build their data platform.
