Through better data lake management, organizations can prevent highly disorganized data lakes from becoming data swamps.
Data lakes offer organizations a methodology for effectively handling big data resources. The information contained in the data lake is available for performing advanced analytics but is not processed until it is needed. A lake provides raw materials that can be used in ways that were unanticipated when the data was collected.
The rationale behind the creation of data lakes is to save all available enterprise data to address the uncertainty of what will be important in the future. Eliminating some data streams from being collected and stored may inadvertently throw away valuable information. What constitutes trivial data points today may be vitally important tomorrow to take advantage of new trends or market shifts.
What is a Data Lake?
A data lake is a data repository that stores large and varied sets of raw data in its native format. A lake is an apt metaphor for the way all data is kept in its natural state without performing any filtering or processing before being stored. The raw data can be used by data scientists and analysts in ad-hoc and creative ways for advanced analytics and modeling.
Following are some of the characteristics that make a data lake a valuable resource for obtaining business intelligence (BI):
- Data is not transformed until it is used for analysis.
- Data can be reused many times in its native form for a variety of different purposes.
- Retained data may take on greater importance in the future and be available in the lake when needed.
- Data lakes offer self-service access to enterprise data resources.
- Data lakes enable information to be accessed and explored in innovative ways that may not have been apparent when the data was collected and stored.
How Information Flows in a Data Lake
Four related concepts can be used to describe how information is collected and used in a data lake.
- Raw data is ingested in any format and stored for later use. Organizations may segregate data into multiple lakes based on criteria such as privacy concerns.
- Vast quantities of data are stored and need to be managed and organized. The volume of data can strain on-premises capacity and is often addressed using cloud storage alternatives.
- Raw data is processed to format it for further use. It may be analyzed to some extent at this point, and then pushed back into the lake until it is consumed by BI or other applications.
- Data is consumed from a data lake by business processes and consumers as needed for activities such as predictive analytics or providing input for BI processes. The same data elements can be reused by multiple groups for widely different purposes.
Differences Between Data Lakes and Data Warehouses
Data lakes and data warehouses are two methods businesses use to handle the challenges of managing and using big data productively. Enterprises often use both methods to fully address their information requirements. There are several important differences between data lakes and warehouses:
- Data lakes contain raw and unstructured data whereas the information in a data warehouse is structured.
- Schema-on-read processing is performed on a data lake versus the schema-on-write processing done in a data warehouse.
- Specialized software and hardware make data warehouses more expensive than data lakes.
- The unstructured nature of the information in a data lake makes it more agile and able to address multiple and varied requirements.
- Data warehouses are used primarily by IT and business users. Data scientists are one of the main consumer groups for data lakes.
The two data storage methodologies complement each other and provide enterprises the means with which they can exploit the value of their data resources.
Effective Data Lake Management
While it is not particularly difficult to create a data lake, efficient data lake management can be a complicated and challenging endeavor. Extracting the business value contained in a data lake requires the right tools. Without proper management, a pristine data lake can turn into a toxic data swamp that simply wastes storage space and provides no benefits to the organization.
Managing data lakes requires implementing processes to address the complexity of big data assets.
Data lake management tools specifically concentrate on several challenging aspects of managing big data:
- Management requires the integration and organization of data from multiple sources and storing it so it can be accessed by end-users.
- Governance of data assets is required to ensure that information is properly cleansed and can be trusted when consumed for analytics and other applications.
- Security needs to be implemented to protect sensitive data residing in the data lake. Personal and customer data needs to be identified so it can be protected to comply with privacy regulations.
Tools like Qubole can help manage a data lake so its information can be used more effectively to meet business requirements and uncover new trends and insights.
It helps simplify the administration of enterprise data lakes with features like automated cluster management. The platform provides performance and stability monitoring and can generate alerts to ensure uptime. Advanced capabilities can recommend performance improvements to more efficient analytics.
Data lakes offer businesses a flexible resource from which to extract BI and perform advanced analytics. Organizations need to explore how the effective management of a data lake can improve their ability to compete in a data-driven market.