With the rapid advancement in technology all over the world, so many business owners are now in search of a better way to ensure that organizational data and information are kept safe and organized. One way through which businesses are doing this is through the use of Data Lakes, which helps to create a centralized place management infrastructure that allows every organization to manage, store, analyze and classify their data.
What is a Data Lake?
Data Lake a storage repository which holds a large amount of raw data in its native form until businesses identify its use. A Data Lake provides the flexibility needed to store raw data and a common pool to combine multiple points and shape the data to provide useful insights that can be customized to meet the customers need and requirements.
Data Lake can also be seen as a platform that allows the efficient storage of data and support tools for understanding data from quick exploration to advanced analytics. A Data Lake is governed by a standard; this is done to keep track of the lineage, enforce security, and for centralized auditing.
All organizations need a d Data Lake because it enables them to merge different data silos and also provide a representation of an organizational data asset. In order words, a Data Lake lays the foundation for data science that would otherwise be difficult to derive without a database.
Benefits of a Data Lake
Some of the business benefits of Data Lakes include the following:
• Ensure data availability at all times: A Data Lake ensures that all employees no matter their designation have access to data. It gives room for the democratization of data, which implies that data is available not just to managers but to all employees irrespective of their levels or designation. All employees have access to all, and they also have the option of only using those that are essential in meeting their business or department needs.
• Cheap Scalability: One of the biggest benefits of a Data Lake to the enterprise is the ability to keep a large amount of data for a considerable price, which is less than a managed data enterprise warehouse.
Usually, when looking out for solutions, one of the factors put into considerations by various organizations is the storage cost; Data Lake offers sound financial values. However, it is vital to state that despite being cheaper than the data warehouses, Data Lake needs to have some form of formal organization during processing and analysis.
• Chance to legitimately horde organization data: Because the use of Data Lakes is cheap, and it offers a huge variation in data types, companies have the chance to hoard their data. This implies that if a particular data is of no value now, a company can store it for the future when will have significant value. Also, Data Lakes allows data to be fed through in native format, which means that it can be stored and added over time, making it more useful for future analysis.
• Gives room for future tech changes: In the last few years, there have been radical changes in data technology, and that is why Data Lake is of great importance to every business. 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 it easier to pull through necessary data to any required system in the future; it also negates its cost when moving data to legacy systems.
• Provides quality data for real-time analytics: Due to the processing power of Data Lakes and the tools used, various departments can have access to quality data. This is because Data Lake leverage on the large quantities of data and deep learning algorithms to arrive at real-time decision analytics.
• Data Lake supports SQL and other languages: Although the traditional data warehouses technologies support SQL, which is good only for simple data analytics. In advanced cases, a Data Lake is employed because it offers various options and language support for analysis; it also provides features to tackle advance requirements.
• Preserves raw data for exploration: Data Lake has the capability of preserving raw data for data exploration and data science. It provides analytics environment for data scientist where data exploration and data related task can be performed without waiting for the IT department to model data and load them.
• Handling data at speed: Data Lake also handles data at high speed. It makes use of reliable tools like Spartk Streaming, Storm, Flick, Kafka, which are built for scalability and speed to handle data and produce a fast result.
• Versatility and Scalability: Unlike the traditional data warehouse, Data Lake offers scalability at a very cheap price. Data Lake makes use of a scalability tool known as Hadoop, which leverages the HDFS storage to handle a growing amount of data and accommodate data growth. It is also versatile, which implies that it can be used to store both structured and unstructured data from diverse sources. It can store multiple media, chat, social data, binary, and any other form of data.
Data Lake vs. data warehouse
Data Lakes and Data Warehouses are used basically for storage of big data. However, they differ in all ramifications. While Data Lake is used for the storage of for raw and unprocessed data, Data Warehouse, on the other hand, is a repository for storage of filtered and structured data processed for specific purposes.
Some of the major differences between Data Lakes and Data Warehouses can be highlighted by considering the following points
• 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 requires much storage capacity than the data warehouse.
However, Data Lake is sometimes turned to data swamp where all forms of data are dumped without the right data quality and data governance put in place. By storing only processed data, data warehouse, on the other hand, saves a lot of storage space.
• Purpose: Another difference between Data Lake and Data Warehouse can be determined in terms of purpose. The purpose of data stored in Data Lake is unknown; most data which flows in a Data Lake are even stored for future use, which implies that Data Lake has less organization. However, a data warehouse only stores processed data which has specific use within an organization; this implies that storage spaces cannot be wasted on data that may never be used.
• Users: Data Lakes are often used by data scientists who are familiar with unprocessed and raw data; these people have specialized tools needed in understanding and translating such unprocessed data for business use. However, a data warehouse is used by business professionals in forms of tables, charts, spreadsheet, and others, almost all employees in an organization can read processed data that are stored in a data warehouse.
• Accessibility: Another difference between the Data Lake and Data warehouse can be seen in terms of accessibility and ease of use. Data Lakes are easy to use and change because they lack structure. However, data warehouses are more structured, which implies that the processing and structure of data itself make the data easier to decipher, while the limitation of data makes the Data Warehouse more costly to manipulate.
• Data Types: While Data Warehouse consists of data extracted from transactional systems and qualitative metrics, it 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 these 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.
• Adaptability: While Data Warehouses are quite difficult to change because they are processed data and a considerable amount of time need to be spent in developing the warehouse structure, Data Lake, on the other hand, is accessible to anyone who needs them.
• Insights: Because Data Lakes contain all forms of data and enable users to access data before it been transformed, Data Lake enables users to get a fast result than the traditional Data warehouse. Even though this early access to data usually comes at a price.
Data Lake vs. Data warehouse: which should you choose?
Almost every organization in various sectors of the economy needs both the Data Lake and the Data Warehouse. A closer look at these organizations will show how Data Lake and Data Warehouse are used.
• Health care industry: In the past, companies under the health care industry used a Data Warehouse in data storage, which was never successful due to the unstructured nature of data in the health care sector. Such data include clinical data, physician notes, among others. Data Lake became the best option for the health care industry because it can store unprocessed and raw data
• Education: In the education sector, Data Lake also offers flexible solutions because the value of big data in the education sector has become quite obvious. Data about student grades, attendance, and others which are in their raw forms can only be stored by a Data Lake.
• Finance: In the financial institutions, the use of data warehouse remains the best solution because data from all financial institutions are usually structured for access by the employees rather than data scientist.
• Transportation: In the transport sector, Data Lake is used to store raw and unprocessed data, especially in supply chain management. The use of Data Lakes for the storage of data in the transport sector radically helps in cutting down the cost of data storage.
Importance of Data Lake
Some of the importance of Data Lake includes the following:
• Offer access to a huge sum 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; it focuses on providing businesses with unfettered access to information which doesn’t decimate based on perceived data importance.
• Store all forms of data: Data Lake stores all forms of data, unlike the traditional data warehouse that lets business owners and professionals down by categorizing information narrowly and flushing out a lot of insight which would have been quite useful to businesses; this does not only cost business much capital it also squanders opportunities they would have profited from.
• Easily Shareable: Data stored in a Data Lake are accessible to all which gives it an advantage for sharing across the enterprise; this will be of great importance in future as more teams have the right skill needed for in-depth data analysis. • Cheaper: The “store everything” approach of the Data Lake makes it quite cheaper than the traditional data warehouse. Therefore entrepreneurs who want to cut cost will find Data Lake as an irresistible option.
Finally, a Data Lake is the new norm for all enterprise due to so many benefits. However, the rise of Data Lake may be slowed because so many organizations are still unaware of the best practices involved when it comes to handling large forms of data.