What is a Data Lake?
Data Scientists can do amazing things with mathematical models. Yes, they can actually predict the future. They can tell you things you did not even think to ask about, but to do this, they need something. They need data. Lots of it. Preferably all in one place – a Data Lake.
A Data Lake is a consolidated data store that allows storage of structured, semi-structured, and unstructured data. It is stored without restructuring. Data analytics are performed, and the results are shown in dashboards and visualizations. Data analytics can be simple or complex.
Why you need Data Lake?
Remember the first time when you were a kid and you heard the expression, Knowledge is Power? That is why you need a Data Lake.
Data Lake leads to knowledge. It is used to gather business insights and make analytical predictions. A company which produces business benefits from their data will outdo their competition.
Data leaders can identify and act quickly on business opportunities by attracting and retaining customers, boosting productivity, and making knowledgeable decisions.
Value of Data Lakes
Better and faster decision making is the result of data analytics performed on data within a Data Lake.
Examples of Data Lakes adding value:
1. Better customer interactions
A Data Lake combines customer data from CRM platforms with social media analytics, buying history, and incident tickets, helping to learn the:
– Most profitable customer cohort
– Cause of customer churn
– Promotions or rewards that will increase loyalty
2. Improved Research & Development (R&D)
A data lake can help your R&D teams:
– Test their hypothesis
– Refine assumptions and assess results
– Perform genomic research leading to more effective medication
– Understand the willingness of customers to pay for different products
3. Increased Efficiencies
The Internet of Things (IoT) introduces more ways to collect data on processes like manufacturing, with real-time data coming from internet-connected devices.
A data lake makes it easy to:
– Store and run analytics on machine-generated IoT data
– Discover ways to reduce operational costs
– Increase quality
Data in a Data Lake
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. A Data Lake architecture solves this.
A Data Lake is an architectural approach facilitating the storage of massive amounts of data into a central location. It is available to be categorized, processed, analyzed, and consumed by different groups within an organization. Since data can be stored as-is, there is no need to convert it to a predefined schema.
How a Data Lake helps:
1. Collect and store any type of data, at any scale, and at a reasonable cost
2. Prevent unauthorized access
3. Catalogue, search and find
4. Perform new types of data analysis
5. Use a broad set of analytic engines for ad hoc analytics, real-time streaming, predictive analytics, artificial intelligence (AI), and machine learning
A Data Lake extends an existing data warehouse. A data warehouse is structured, related data, often from transactional source systems. It can be used for structured, semi-structured, and unstructured data, from anywhere or anything.
Building a Data Lake
Cloud providers enable access to a big data platform used during the creation of the Data Lake. Provided is secure infrastructure with a broad set of scalable, cost-effective services to collect, store, categorize, and analyze your data and obtain business insights.
Advantages of a Data Lake
Easily ingest data in a variety of ways. Store data, regardless of volume or format, using Amazon Simple Storage Service (Amazon S3) or Azure Data Lake Storage (ADLS) Gen2.
Data Lake infrastructure can be deployed in moments. Teams no longer spend time configuring the setup which makes them more productive. It is easy to experiment with innovative ideas. Projects roll out better and faster.
3. Security, Governance, and Compliance
Cloud providers meet stringent requirements. Environments are constantly audited for certifications such as HIPPA, PIPEDA, GDPR, CCPA, HiISO 27001, FedRAMP, DoD SRG, and PCI DSS.
4. Comprehensive Proficiencies
Build big data applications and develop workloads regardless of data volume, velocity, and variety.
AWS and Azure each have dozens of services which reduce the undifferentiated heavy lifting associated with big data, data model, and data lakes. They have everything needed to collect, store, process, analyze, and visualize.
AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. Creating a data lake with Lake Formation is as simple as defining data sources and what data access and security policies you want to apply.
Lake Formation then helps you collect and catalog data from databases and object storage, move the data into your new Amazon S3 data lake, clean and classify your data using machine learning algorithms, and secure access to your sensitive data.
Users can access a centralized data catalogue which describes available data sets and their appropriate usage. Your users then leverage these data sets with their choice of analytics and machine learning services.
Microsoft Azure Data Lake
Azure Data Lake has all the capabilities that are required for it to make easy for developers and data scientists to store data of any size, shape, and speed. It allows for all types of processing and analytics across platforms and removes complexities of ingesting and storing data. It integrates with operational systems and data warehouses allowing a user to extend current data applications.
Data Lake Benefits:
1. Build data lakes quickly in the cloud (AWS or Microsoft Azure)
2. Simplify security management
3. Provide self-service access to data
Data Lakes – A Game Changer
Store all your data in one place. Use it to gain knowledge, power, and the ability to predict the future. The data scientists who make you rich will thank you.
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