Introduction to Data Lakes

Data lakes are cheap storage environments that keep petabytes of unprocessed data. In contrast to data warehouses, data lakes support "schema-on-read" storage, so they can store unstructured and structured data. Data scientists, developers, and data engineers can use the data for machine learning (ML) projects and data discovery exercises thanks to the flexibility of the storage requirements.

While early adopters learn about data lakes' benefits, others might become pits or swamps of useless datasets. A data lake that isn't managed properly using standard data governance and quality checks to produce insightful learning results in a data swamp. Data kept in these storehouses becomes useless if it does not provide proper oversight. While the origin of the data problem is unknown, data pits provide minimal value to the business.

Data Lake Vs. Data Warehouse

Although both data warehouses and lakes keep data safe for use, they are unique in their capacity to store data. Each has specific storage needs that make it the best option in certain situations. For example, data warehouses must have a defined schema to satisfy specialized data analytics needs for outputs like dashboards and data visualizations. Business users and other pertinent collaborators who frequently use the report deliverables specify these requirements. The underlying organization of a data warehouse is typically a relational system, with data coming from transactional databases.

The combination of data from non-relational and relational systems in data lakes enables data scientists to include both unstructured and structured data in a wider range of data science projects.

Each system has its own benefit and flaws. For instance, data warehouses are more effective but more expensive. Data lakes are cheap even though they return query results more slowly. Also, the capacity is small and perfect for small businesses.

What Separates a Data Lake From a Data Lakehouse

The flaws of data lake and data lakehouse are forcing a convergence of these technologies, even though adopting data lakes and data warehouses will only grow as new data sources become accessible. Data lakehouse combines a data lake's low-cost benefits with a data warehouse's data structure and management capabilities.

The Use Cases For Data Lakes

The business purpose of the data need not always be specified at the outset because data lakes are used to store enormous volumes of unprocessed data. However, the following two examples of data lakes' principal use cases:

Proof of Concepts (POCs)

POCs projects benefit greatly from storage in a data lake. Machine learning models benefit greatly from being able to house different data types because it enables them to integrate unstructured and structured data into a predictive model. This can be helpful in situations where data scientists cannot use relational databases, such as text classification. In addition, data lakes can serve as a proving ground for other huge data analytics initiatives. This can include building large-scale dashboards or supporting IoT applications, which typically require real-time streaming data. The data can be processed using ETL or ELT after its value and purpose are determined to store in downstream systems.

Data Recovery

Due to their substantial storage ability and affordable storage prices, data lakes can be used as backup storage to mitigate the effect of disaster. They can be helpful for auditing data to maintain quality by enabling checks because data is saved without transformations. Teams can cross-check the work of previous data owners, which is especially helpful if a data warehouse lacks documentation for its data processing.

A data lake can also be used to cheaply store cold or inactive data that can later be used for regulatory inquiries or net new analyses because the data there doesn't have to be used immediately.

Benefits of a Data Lake

More Adaptable

Data lakes are perfect for advanced analytics and machine learning projects because they can accommodate both structured and semi-structured datasets.

Cost

Less money must be spent on hiring staff because data lakes do not require as much upfront planning (such as the definition of a schema and a transformation). In addition, when compared to other storage repositories like data warehouses, data lakes have lower actual storage costs. Businesses can now allocate their funds and resources more effectively across various data management initiatives.

Scalability

Data lakes can aid in business scaling in two different ways. Due to their self-service capabilities and overall storage capacity, data lakes are more scalable than other storage services. Data lakes also act as a testing ground for employees to create successful proofs of concept. It is much simpler to automate a workflow on a larger scale once a project has demonstrated its value on a smaller scale.

Fewer Data Silos Exist Now

Data silos exist within businesses in various industries, from healthcare to the supply chain. Because there is no longer a single owner of a particular dataset, those dependencies start to disappear as data lakes ingest raw data from various functions.

Enhanced Client Experience

Successful proof of concept can enhance the overall user experience by enabling teams to better understand and personalize the customer journey through novel, insightful analyses, even though this benefit won't be immediately apparent.

Problems with a Data Lake

Data lakes have many benefits, but they also have some disadvantages. They include:

Performance

Since a data lake is already slower than other data storage systems, performance suffers as the amount of data it receives increases.

Governance

While data lake's capacity to use data from numerous sources gives businesses a competitive edge in their data control strategies, strong governance is also required to manage. Data should be classified and tagged with pertinent metadata to prevent swamping and enhance accessibility, allowing less technical team members, like analysts, to use self-service functionality. Finally, security measures like acc.ess controls, data encryption, and other precautions should be implemented to ensure compliance with privacy and regulatory standards