Understanding Data Lakes
Data lakes are vast collections of structured data, which can be either public or private, analytical or demonstrative. They make storing and processing large volumes of structured data easier through an ecosystem of IT tools and processes, such as infrastructure as a service and software as a service. This ecosystem consists of key components like software tools and processes, IoT connected devices, storage system providers, and data integrator partners. However, it should be noted that data lakes are different from data warehouses. Despite the advantages data lakes offer, they also present challenges that need to be addressed.
Understanding Data Lake Architecture
A data lake is a centralized platform for storing and analyzing vast amounts of data from different sources, such as social media or web logs. It offers easy access and analysis of data, from sales transactions to employee performance reviews. To create a data lake, there are different technologies, including databases, NoSQL databases, and cloud storage to be used. However, when creating a data lake, it is advisable to save only the important data to avoid loss or deletion. For less important data, a separate database can be used.
Why Use Data Lakes?
A data lake is a storage system for all data generated by an organization, including databases, images, and videos. It allows efficient data analysis and prediction of future trends. The main purpose is to store all company data in one place for easy access and decision-making. Benefits include quick data access, large storage capabilities, and reducing data repetition. Reasons for constructing a data lake include organizing data from various platforms for a holistic view, direct use of data from BI tools, faster querying, and loading data into one place for easier BI tool use.
Data Lake Architecture
A Data Lake Architecture is a process for designing and managing a data lake ecosystem. It includes key components, like ETL tools, a data warehouse, and a reasoner that works together to provide the final output. A robust Data Lake Architecture model has several primary aspects:
* Monitoring and supervision of data lake operations to measure performance and improve its efficiency.
* Security considerations are critical in the initial phase of designing a data lake architecture.
* Metadata refers to data associated with reload intervals, structures, etc.
* A single organization often has multiple stewards responsible for data management.
* Ability to track and manage ELT processes to transform raw data before it reaches the application layer.Key Components of Data Lake Architecture
A data lake ecosystem has different types, including public, hybrid, and private. An organization should define its data lake structure and components. Here are the five main components:
1. Ingestion Layer: This layer grabs raw data and processes it to be ingested into the Data Lake. The processed information must be transformed into the application-required output.
2. Distillation Layer: The Distillation Layer transforms structured data into an ingestible format to meet business needs. This layer cleanses the data to be easily ingested.
3. Processing Layer: This layer sets the architecture of data stores and analytical tools that process it. It validates and loads data into relevant tables that use validated data to produce the desired output.
4. Insights Layer: This layer uses the query interface of the Data Lake based on SQL and NoSQL queries to obtain data. Visuals like graphs, dashboards, and reports help users understand complex data trends, facts, and query efficiency.
5. Unified Operations Layer: The layer audits system performance, collects data, processes it, and stores the results in the Data Lake. Data scientists can use sandbox environments to test new models and collect feedback.
Differences Between Data Lakes and Data Warehouses
Data lakes hold all data in one location and transform it through ETL tools while data warehouses hold data in various locations and create a warehouse through ETL tools. Both models have limitations but data lakes lack granularity and security options. Data warehouses provide flexibility for low-cost and open-source tools but are only suitable for specific industries requiring low-fidelity data analytics.
| Data Warehouse | Data Lake |
|---|---|
| The relational model is used in operational databases, operational analytics, and line of data business applications. | Relational and non-relational data from IoT devices, websites, mobile apps, social media, and corporate applications. |
| The schema was designed before the data warehouse implementation (schema-on-write). | The schema is written at the time of analysis (schema-on-read). |
| Query results are fastest when using high-cost storage instead of disk. | Swapping to cheaper storage has helped improve query results, according to analysis. |
| The central version is a highly curated version of the truth. | Raw data may or may not be curated. |
| Business analysts | Data scientists, data developers, and business analysts utilizing curated data. |
| Examples of usage are reporting, visualizations, and batching data. | Machine learning can be used for data discovery and profiling. |
Overall, data lakes and data warehouses have their own unique advantages and disadvantages, and choosing the right model depends on the needs and goals of the organization.
Benefits of Data Lakes
Data Lakes are computer storage systems that collect, store, process, and analyze data. They bring several benefits such as faster data access, higher accuracy, and reduced storage costs. The data is stored in one location, and users can access it with ease, resulting in faster decision-making.
Data Lakes are not intended to permanently store data; instead, they transform or process data and create new datasets to be stored elsewhere. They offer an ecosystem of open-source and licensed software like Spark, Hadoop, and SQL, which allow for a large number of plug-and-play solutions.
The Data Lake enables real-time decision-making, stimulates innovation by allowing data engineers to experiment with new algorithms and data sources, and provides a platform for future growth. The Data Lake offers limitless possibilities for real-time data analytics.Challenges of Data Lakes
Data lakes have become a popular choice for storing data but come with their own set of challenges. These challenges can impact the design, development, and use of data lakes. Some of these challenges include:
– Limited data management: While data lakes are great for storing large amounts of data, managing such data can be challenging. Data governance best practices that help manage collected data are often overlooked, which can lead to security and privacy issues.
– Training for new members: New team members may not be familiar with the tools and services required, necessitating training to use them effectively.
– Difficulty integrating third-party data: Acquiring and converting data from third-party sources into a format that the data lake engine can process can be challenging.
– Ongoing investments: Data lakes must be regularly monitored and updated with duplicate data removed. Scaling the platform as the need arises is crucial to prevent underutilization.
Data lakes are a useful solution, but enterprises must invest in the process for long-term success.
The Benefits of Data Lakes for Organizational Data Management
Data lakes offer a centralized and accessible location for structured data storage in organizations. They can also host other data types and act as a staging area for data analysis. Additionally, non-technical staff can be involved in data analysis. Data lakes are not limited to structured data and can store unstructured data such as images, videos, and financial data. The ecosystem of technologies and processes around data lakes simplifies the storage and processing of large volumes of data.
Additional Resources
Here are some additional resources to help you learn about DBMS:
