Data lakes allow storing of unstructured data to let the scientists extract the retrospective information related to the new hypothesis or the business idea at any time. This makes data lakes an indispensable source of information even nowadays. To make the data extraction from the data lake, quite often it’s necessary to invite advanced Python developers.
Another way to gain the data is to use the ready frameworks. Hadoop and Spark are among the most popular ones. The advantages over other frameworks: they are open-source and can be used in almost any environment.
Before you start preparing data, it’s worth understanding how much data it needs. This means defining the daily, weekly, or monthly information you will download. And to evaluate how often the updates should be implemented. As with simple storage, we are very quickly coming to the issue of memory expenses. It’s better to work out the compromise between too many data records and not enough information.
Several types of analysis are used effectively in a data lake environment. Some popular types include machine learning, statistical-based analysis, standard text mining, predictive modeling, and analytics and SQL queries.
What problems can users face when applying data lakes?
Organizing the data lake storage is essential for analyzing business success; it’s crucial in small, large, and medium-sized businesses. But professionals often encounter difficulties in managing and analyzing data. It’s worth highlighting the main challenges that managers face.
Data swamps. This problem is the most acute. Not turning the data lake into a swamp when working with information is essential. That is, it is necessary to keep control over the data processing, transfer, and storage. Otherwise, the researchers will get a lot of unnecessary information, which will significantly hamper the extraction process. As a result, the lake resembles a swamp, which contains a lot of information, most of which is simply unnecessary.
Suppose you are working with a particular problem and know the data to be monitored in advance, then not the data lake you need to organize but a data warehouse. There the user quickly navigates through the information and finds what they need. This has a positive impact on the speed and quality of the workflow.
Technology overload. A large variety of technologies is an advantage for the researcher, but at the same time demands more advanced developers or multiple staff to support each other in implementing the data extraction and analysis.
Unexpected costs. It is important to determine cost planning at the outset. Further, it is essential to take high-level data management control to keep costs from increasing. It’s common for a firm to receive a hefty bill for data storage. Sometimes business owners are unprepared for these costs. Scaling lakes will also lead to additional costs. But this is sometimes just necessary for the entire operation of the firm.
Data management. A data lake is for storing data that is still raw and for using it in analytics. Data management is one of the main tasks to keep the data safe and available when needed.
Data lake providers
Hadoop and Spark are both products of the Apache Software Foundation and are open source. The Linux Foundation also offers open-source coding. Choosing a data lake provider is not an unimportant point. There are free open-source versions. But most users prefer commercial versions, where the vendor will provide technical support to the user. It is also actual to own software from suppliers.
The difference between vendors is that some specialize in providing a complete platform, while others specialize in providing tools to work effectively with data lakes.
It’s worth noting the following vendors:
- AWS. The vendor offers the AWS Lake Formation tool, which is used in data creation, and AWS Glue, which is required for integration.
- Cloudera. The platform is deployed in both public and hybrid clouds. The vendor provides support services.
- Databricks. The founder is Spark. The vendor offers elements for storage and data lakes.
- Dremio. The platform supports interactive queries and can be used as a managed cloud.
- Google. Google Cloud Data Fusion, used for integration, is available, as well as a list of services to move lakes to the cloud.
- HPE. It supports Hadoop environments.
- Microsoft. There is not only Azure HD Insight and Azure Blob Storage available but also Azure Data Lake Storage Gen2.
- Oracle. Its data lakes contain a large amount of data for Hadoop and Spark. Management tools are an additional option.
- Qubole. It’s a cloud platform for Qubole, which helps manage and design data efficiently. Ability to support analytical applications.
- Snowflake. It offers cloud storage and allows you to work with data lakes.
- The advanced data management team is a good helper if you need professional support for organizing and managing your data storage properly.
Wish to know more about other data storages, including the most advanced, data lakehouse? Check the latest publications from serokell.io/blog.