What is Data Lake? How is it different from a Data Warehouse? Do you need it, and why? How do other companies use Data Lake in practice? Let’s find answers to these and other questions.
What is Data Lake?
Data Lake is a centralised repository designed for storing, processing, and securing large amounts of data. It encompasses structured, semi-structured data, and unstructured data.
It can store data in its native format and process any variation of it regardless of size. Because Data Lake is available on Google Cloud, you can easily harness its capabilities.
With Data Lake, you gain access to a scalable and secure platform that allows you to:
- Acquire any data from any system at any speed.
- Collect data from on-premise, cloud, and edge computing solutions.
- Store any type and quantity of data with full fidelity.
- Process data in real-time or batch mode.
- Analyse data using SQL, Python, R, or other data languages and analytical applications.
Data Lake vs Data Warehouse
The simplest explanation of the difference between Data Lake and a data warehouse is that the former is not just a storage space. While both involve storing data of a certain capacity, they are optimised for different uses.
- Unlike Data Warehouses, Data Lakes are optimal for all types of raw data stored in the cloud and not only structured data like a data warehouse. If you collect a lot of data assets, like images, sensor readings etc., but are not yet ready to process or haven’t yet decided on the best way to apply big data analytics tools, data lakes are where you can keep all your data safely.
- Data lakes typically store raw data in the exact format that they received from data sources. It does not need to be standardised for big data processing, advanced analytics or business applications yet.
- Because of their nature as vast data storage spaces, data lakes grow big. After all, you can store swathes of historical data that your analysts may want to review at some point. This is why scaling and optimising data storage capacity is even more important with Data Lake architecture than with Data Warehouses.
In short, Data Lakes provide a centralized repository for all your raw data, regardless of its format and future purpose. Meanwhile, data warehouses are most suitable for business users that create repeatable reports and analyses, such as monthly sales reports, processing sales data from a specific region, or monitoring website traffic.
As you may have already guessed, Data Lakes and Data Warehouses are complementary tools, not competing with each other.
Do I need a Data Lake?
The first thing you need to figure out is what data types and data sets you work with daily. Then, answer questions about what you want to do with this data, how complex the data acquisition process is, and whether you have a data management strategy at all.
Data Lake is applicable when you want more than just storage; you want to better understand business events using that data. Because you preserve the original format and type of data from the source, Data Lakes provide more context, allowing for more precise analytical experiments.
Moving data to a Data Lake allows you to:
- Lower Total Cost of Ownership (TCO).
- Simplify data management.
- Prepare the company for artificial intelligence and machine learning applications.
- Speed up data analysis.
- Improve security.
Examples of Data Lake usage
Currently, some of the biggest users of Data Lake technologies are Artificial Intelligence and Machine Learning scientists. This is because unlike people, AI thrive on vasts amounts of unprocessed data, in various output formats. Oftentimes AI-powered predictive analytics models produce priceless spot-on insights, because the data they use is plentiful and raw.
However, there are also many traditional applications for Data Lakes:
Media and entertainment
Traditional cloud Data Lakes are the right solution for companies collecting and offering streaming data, like music and podcasts or online radio. Their capabilities enhance recommendation systems, making users more likely to use a company’s solution when they receive valuable content tailored to their preferences. This translates into better Optimization, e.g. by selling more advertisements.
Telecom companies experience constant customer fluctuation. As the expiration date of a subscription agreement approaches, customers start looking for alternative, cheaper options, especially since the level of service or network access is nearly identical among all operators. Data Lakes enable building churn propensity models, allowing the company to take actions to encourage customers to stay.
Financial and investment-related tools are another good use case of Data Lake technologies. While subject many conditions, including psychological ones, financial data and indicators do follow patterns. Data Lakes provide tools that allow machine learning to influence portfolio risk management by acquiring real-time market data.
Drawbacks of Data Lakes
Traditional data lakes have certain gaps, perhaps a better metaphor would be whirlpools, or even data swamps. Firstly, data processing and analysis consume a lot of resources, which might lead to exceeding Service Level Agreements (SLAs).
Another obstacle is slow analytical experiments and extended development cycles due to resource allocation complexity, administrative challenges, and scaling.
Remember that there is a lot you can do to modernise and optimise your Data Lake. Experts from Google Cloud’s FOTC can assist you in this.
Increasing Data Lake load
You can offload high-data or analytical processing requirements to Google Cloud and take advantage of automatic scaling capabilities.
Building a native Data Lake in the cloud
Another option is to build a native Data Lake in the cloud. Take full advantage of the expertise and knowledge of FOTC Cloud Architects who can create a smoothly operating Data Lake from scratch.