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What is a data lake?
A data lake is a data repository of unstructured, unanalyzed, raw data which can include copies of source system data and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. A data lake can include structured data from relational databases, semi-structured data and binary data. A data lake accepts and retains all of the data from all data sources and supports all data types. Schemas are applied only when the data is ready to be used.
Why is having a data lake important?
Having a data lake is important because it gives organizations the ability to increase efficiencies with data management, from more sources, in less time. A data lake empowers users to collaborate and analyze data in different ways, which can lead to better, faster decision making. And successfully generating business value from data can lead to revenue growth.
Having a data lake can also enable organizations to perform new types of analytics such as machine learning over new sources such as log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helps them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
A data lake also benefits organizations in the following ways:
- A data lake is highly agile, giving developers those working in data science the ability to easily configure a given data model, application, or query on the fly.
- Data lakes architecture have no inherent structure and are more accessible. Any user can access the data in the data lake—even though the three Vs of data (volume, velocity, and variety) could inhibit less skilled users.
- A data lake is also scalable because it lacks structure.
- Data lakes are not costly to implement since most technologies used to manage them are open source.
- Data lakes are scalable because they are unstructured.
- Both labor-intensive schema development and data cleanup or governance can be deferred until after your organization has identified a clear business need for the data.
- The agility of a data lake enables variety of different analytics methods to interpret all data types (including cloud data) which include the following: machine learning, big data analytics, real-time analytics, and SQL queries.
Data Lake Best Practices
- Quickly onboard data and ingest data early. Early ingestions and processing enable integrated data to be available ASAP to your reporting, operations, and analytics teams.
- It’s better to control who controls which data into the lake as well as when and/or how it is loaded. Without control, a data lake can easily turn into a data swamp. And when you put garbage data in, you’ll get garbage data out which will be of no use for effective decision making.
- Focus on business outcomes. In order to successfully transform your enterprise, you have to understand what is most important to the business. Gaining insight into business intelligence and understanding the organization’s core business initiatives is key to identifying the questions, use cases, analytics, data, and underlying architecture and technology requirements for your data lake.
- Integrate data of diverse vintages, structures, and sources. Blend traditional enterprise data with modern big data in a data lake to enable advanced analytics, enrich cross-source correlations for more insightful clusters and segments, logistics optimizations, and real-time monitoring.
- Update and improve data architectures—both modern and legacy. Since data lakes are rarely siloed and can extend traditional applications—it can be a modernization strategy for extending the life and functionality of an existing application or data environment.