Gathering data is an essential step for business that want to stay ahead of the competition. Data-driven decision making is all the rage. From data science, machine learning, and advanced analytics to real-time dashboards, decision makers are demanding data to make informed decisions. But what can you do with tones of information? There comes Data Lake, which is a centralized repository that allows you to store structured and unstructured data at any scale. Data Lake works in a similar to real lake and rivers; the contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.
The Data Lake democratizes data and is a cost-effective way to store all data of an organization for later processing. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
How Data Lake work?
Once data is in the lake, the data is available to everyone. Data is smoothly ingested into the data lake, where it is managed using metadata tags that help locate and connect the information when business users need it.
Data lake allows for data exploration and discovery, to find out if data is useful or to simply leave it in the data lake while you wait to see how you can use it.
A Data Lake has three main attributes:
Collect everything. It contains all data, both raw sources over extended periods of time as well as any processed data.
Dive in anywhere. It enables users across multiple business units to refine, explore and enrich data on their terms.
Flexible access. It Data Lake enables multiple data access patterns across a shared infrastructure: batch, interactive, online, search, in-memory and other processing engines
Why using Data Lake?
There are many reasons why organizations should start using Data Lake. Here we have listed some of them.
Supports multiple users
The Data Lake approach is that it meets the needs of a variety of users that can have access to the data for whatever needs they have. According to the experts, there are different types of data users which can get into three main categories based on their relationship to the data. The first ones are those who simply want a daily report on a spreadsheet. The second ones are those who do need more analysis but like to go back to the source to get data not originally included, and the third ones are those who want to use data to answer entirely new questions.
Cost-effective storage
Data Lakes are relatively cheap and easy to store because costs of storage are minimal and pre-formatting is not necessary. The “store everything” approach of the Data Lake makes it quite cheaper than the traditional data warehouse. This is a cost-effective and technologically way to meet Big Data challenges.
Data is available at all times
Data Lake favors the democratization of data because ensures that all employees have access to data whenever they need it. All employees have access to all data and they have the option of only using information that is essential for the business or their department needs.
Data is easily shareable
Data stored in a Data Lake is easily accessible and can be shared across the enterprise. This is a big advantage for big organizations where more than one team will need information for an in-depth data analysis.
Easy to use
It offers organizations the chance to store their data in the native format before being transformed into a more structured database for future use. This makes the storage and move easier because there is no need to move data between legacy systems.
Offer access to big sums of data
Data Lakes offer unrivaled access to a huge but navigable sum of data that can be put into productive use in the future. These data repositories provide businesses with unfettered access to information.
Provides data for real-time analytics
Data Lake can leverage on the large quantities of data and deep learning algorithms to arrive at real-time decision analytics.
Supports diverse languages
Data Lake support SQL and various options and languages for analysis and provides features to address advance requirements.
Data Lake vs. Data Warehouse
Data Lakes and Data Warehouses are used basically for storage of big data. However, they have many differences. While Data Lake is used to store raw and unprocessed data, Data Warehouse is a repository for storage of filtered and structured data processed for specific purposes.
Below you will find some of the major differences between Data Lakes and Data Warehouses.
Data structure
Data Lake is used for the storage of raw data which purposes are unknown while data warehouses are used to store processed and refined data. Due to this, Data Lake provides a storage with bigger capacity than the data warehouse. To store only processed data, it is advisable to use data warehouse.
Data types
Data Warehouse stores data extracted from transactional systems and qualitative metrics and ignores data generated from non-traditional data sources like web server logs, sensor data, and social network activities, among others. Data Lakes, on the other hand, embraces nontraditional data types; it keeps all forms of data regardless of the source and structure and transforms them when the organization is ready to make use of it.
Accessibility
Another difference between the Data Lake and Data warehouse is the accessibility and ease of use. Data Lakes are easy to use and change because they lack structure. Data warehouses, on the other hand, are more structured, which means that there are more limitations to process and manipulate data.
Users
Data Lakes are often used by data scientists who are familiar with unprocessed and raw data and have specialized tools required to understand and translate unprocessed data into the type of date used by businesses. Data warehouse is used by business professionals in forms of tables, charts, spreadsheet, and others. Almost everyone within an organization can read processed data that are stored in a data warehouse.
Purpose
Data Lake and Data Warehouse use data with different purposes. Data Lake users do not really know how the stored data will be used which implies that Data Lake has less organization. Data warehouse, on the opposite, only stores processed data which has specific use within an organization; this means that storage spaces cannot be wasted on data that may never be used.
Insights
Data Lakes contain all forms of data and enable users to access data before it been transformed, therefore users can get faster results than the traditional Data warehouse.
How to use Data Lake for business?
Businesses working to become more data-driven are always searching for new ways to efficiently manage data. But massive datasets are not always easy to get under control. Taking a data lake approach can sort those needs out as well as assisting with other critical aspects such as:
-Improving customer relationships
-Enhancing research and development (R&D) activities
-Increasing operational efficiency
The following steps can help you to effectively implement data lakes for your business
Understand the core benefits of data lakes
A data lake provides key capabilities that can help uncover new ways to level up your analytics and inform your decision-making. The overwhelming amount and the variety of data require management. Data governance is critical to standardize the data coming from diverse sources, ensuring data accuracy and transparency, and avoiding data lake dumps.
Leverage data lakes to improve business intelligence
BI is an efficient approach that can allow specialists in your company to use advanced methodologies to work with large volumes of raw data. This helps obtaining meaningful insights, which can improve decision-making and unearth new opportunities for business growth.
A data lake can enhance a BI solution by providing a greater potential for processing data. It can both serve as a centralized source of data for building a data warehouse and function as a direct source of data for BI.
Data lakes have applications in data science and machine learning engineering, where massive datasets are the backbone of technical solutions. In sum, a data lake can become an important pillar of BI and assist in optimizing raw data processing.
Add a structure
To make sense of vast amounts of unstructured data stored in the Data Lake, you should create some structure such as the metadata of a file, word counts, parts of speech tagging, and so on. The Data Lake gives you a unique platform where you have the ability to apply a structure on a variety of datasets, enabling you to process the combined data in advanced analytic scenarios.
Conclusion
Data Lake is increasingly being used to handle big data that in general comes in high volume and takes a long time to process and analyze to get meaningful insights. Having a scalable and centralized solution to store massive amounts of raw data, while having native integration with powerful data analysis tools, is becoming an increasingly essential toolset for business that want to become more data driven in their decision making.
