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Modern enterprises are highly dependent on massive and diverse data, and data centers are the key to generating big data. Over 90% of data is semi-structured or unstructured, so business owners are looking for a way to manage unstructured data. All of this requires a proprietary, organized solution to keep influential organizations' data and information safe. This also requires maintaining capacity through faster processing facilities. And a data lake can be a viable solution.

A data lake is a centralized repository that lets you store all structured, semi-structured, and unstructured data at any scale. Data can be stored in raw format without any data preprocessing, formatting, or cleaning.

What is a Data Lake?

A data lake is a central repository that holds big data from sources in raw format until the enterprise consumes it. The data can be structured, semi-structured, or unstructured and can be used flexibly in the future. This enables data lakes to combine with the various points and shapes of raw data, providing useful insights for customization to meet customer needs.

Store data in a data lake associated with identifiers and metadata tags for a quick rescue. It includes hundreds of terabytes or petabytes for storing simulated data from operational sources including databases and SaaS platforms. A data lake can also be used as a source platform, enabling data storage, and supporting tools to make sense of the data by rapidly exploring advanced analytics. It tracks lineage, imposed security, and centralized auditing that maintains its standards.

Data lakes provide a scalable and secure platform that enables businesses to ingest any data from any system at any speed, whether it comes from on-premises, cloud, or edge computing systems. Store any type or amount of data with full fidelity, in real-time or in batches. Analyze data using SQL, Python, R, or any other language, third-party data, or analytics application.

While both data lakes and data warehouses can store a certain volume of data, they are both optimized for different uses. The two are complementary rather than competitive, so companies may need to use both technologies. Data warehouses are usually suitable for storing repetitive reporting and analytical data common in business activities, such as monthly sales reports, sales tracking data per region, or website traffic.

Do You Need a Data Lake?

When determining whether your company needs a data lake, you should consider the type of data you're dealing with, and what you want to do with that data. The complexity of your data acquisition process, your data management, and governance strategies, and the tools and skill levels used by people in your organization.

Companies are starting to look at the value of data lakes from a different angle, that is, data lakes are not just used to store full-fidelity data, they can also help users gain a deeper understanding of business conditions because data lakes provide richer context than ever before, which helps speed up analysis experiments.

Data lakes were primarily developed for processing large volumes of big data, and companies can often move raw data into the data lake via batch and streaming without the need to transform it.

Enterprises are conducting new types of analytics, such as machine learning, through new sources such as log files, clickstream data, social media, and Internet-connected devices stored in data lakes. This helps them identify business opportunities and act faster by attracting and retaining customers, increasing efficiency, proactively maintaining installations, and making informed decisions.

• The lower total cost of ownership.
• Simplify data management.
• Prepare for the inclusion of artificial intelligence and machine learning.
• Speed up analysis.
• Enhance security and governance.

• Data remains available and ensures that employees can access it whenever they need it.
• The cheap scalability of storing large amounts of data adds financial value to a business, even if it requires some formal processing and analytical direction.
• Data lakes provide variation, and companies can stockpile data in the future because it is kept in a native format, so it can be used and added multiple times without restrictions.
• Adapting to inherent changes based on advances in data technology makes it easier to restore necessary data in the future.
• The utilization of data lakes enables real-time analytics by providing high-quality data and deep learning algorithms to emphasize decision-making analysis of the business.
• The flexibility to support SQL and other programming languages meet advanced requirements.
• The abundance of resources is another benefit, as the data stored in the data lake can come from multiple sources and multiple media, chat, social data, binary, or any other format.

• Data Lake Architecture: A source for collecting data, making it available through ETL for consumption.
• Security: Manage data security.
• Directory Management: Index large amounts of data for easy review.
• Governance: Create replicas of data and management.
• Access Control: Many user groups may simultaneously access data in the data lake at the same time, so proper access control management is important.
• Consistency: Data consistency is important. The data is constantly growing and may be updated in real-time, so if multiple targets use the same data, the data for each target should be consistent.

Data Warehouse vs Data Lake?

Data warehouses are used to store structured data that business professionals can access to create dashboards and data visualizations. A data warehouse is expensive to store and less flexible than a data lake. But it is more mature and stable.

Data lakes are used to store unstructured data in raw format. This data is primarily used for analytics and predictive modeling by data scientists and machine learning engineers. Compared with a data warehouse, data lake storage cost is lower, more flexible, and reconfigurable.

What are the Usage Scenarios of the Data Lake?

Because data lakes provide the foundation for analytics and artificial intelligence, businesses across all industries are using data lakes to increase revenue, save money, and reduce risk.

• Media and Entertainment: Companies that offer online streaming of music, radio, and podcasts can increase revenue by improving their recommendation systems so that users consume more of their services, allowing companies to sell more ads.
• Telecommunications: Multinational telecommunications companies can save money by building churn propensity models to reduce customer churn.
• Financial Services: Investment firms can rely on data lakes to power machine learning so they can manage portfolio risk as soon as real-time market data is available.