Data is crucial for enterprises to grow, prosper, and even exist. Businesses stay ahead of the competition by using data insights to predict trends and market conditions. Consequently, data management becomes essential for enterprises.
Data warehouses have been the single source for all the data within an organization. But they are expensive and ineffective in handling many modern-day use cases. That is when data lakes emerged as a savior in response to the limitations of a data warehouse. Data lakes store open format data, allowing multiple applications to take advantage of the centralized data.
What are Data Lakes?
Data Lake is a centralized data storage system that stores unstructured data in its raw format. The difference between a data lake and a data warehouse lies in the fact that a data warehouse stores hierarchical data in files or folders, while a data lake uses object storage and flat architecture to park massive amounts of data. The data lake architecture’s object storage approach adds unique identifiers and metadata tags to every data unit, making detecting and retrieving data across the spectrum easier. In addition, the inexpensive open formats and object storage allow various applications to take advantage of the data.
We can run loads of analytics on a data lake, such as visualizations, real-time analytics, big data processing, and even machine learning. Together, all the findings guide us to make better decisions and improve performance.
Formal schemas are not imposed on a data lake. A data lake can ingest data in all stages of evolution, whether native data, intermediate data tables, or structured tabular database tables. Additionally, data lakes can process media files like images, videos, and audios, which are crucial to contemporary analytics and machine learning use cases.
Usually, data lakes are built on inexpensive commodity hardware. The scalable hardware can either exist on a desktop or in the cloud.
Why do we need data lakes?
Modern-day architectures don’t like proprietary systems. The open format, high durability, low cost, and scalability make data lakehouse architecture lucrative. Enterprise strategies have shifted to machine learning and advanced analytics on unstructured data. Moreover, data lakes’ ability to consume structured, unstructured, and semi-structured data makes them a favorable choice for enterprises today.
- Centralize data: Consolidated and cataloged data eliminates multiple security policies, data duplication, and collaboration issues. Users downstream find all data sources under one roof without a hitch.
- Smooth integration: Data Lake integrates diverse data formats, including image, video, batch, binary files, and more. New data is welcomed and retained forever in a data lake, so it’s always up to date.
- Self-service tools offered to users: Data Lake provides users a diverse set of tools, skills, and languages to run various analytics tasks instantly.
If businesses don’t stir up value from their data, their peers will outperform them.
How do Data Lakes work?
The following are a few key features that will help us understand how data lakes work:
Ingesting: Data is collected from various data sources and loaded into the cloud data lake. Data sources may include Web servers, databases, emails, FTP, or IoT. Different ingestion methods like batch, one-time, or real-time are used to load structured, semi-structured, or unstructured data.
Storage and Governance: Data lakes employ cost-effective and scalable storage that ensures faster access to explore data in various formats. Enterprise data is governed by a process that looks at how usable, available, accurate, and safe the data is.
Security: Each layer of the data lake is secured to stop unauthorized access. Security features like authorization, accounting, and authentication are essential to the Data Lake.
Discovery: In this stage, data is prepared by tagging techniques. The data lake then consumes data by interpreting and organizing the prepared data.
Exploration: Data analysis starts at this stage. Identifying the correct dataset begins the data exploration cycle.
Benefits of a Data Lake
Data lake architecture lays the foundation for enterprise-level data science and modern analytics applications. They help businesses manage operations well by identifying the latest trends and opportunities. For example, prediction models on consumer behavior can refine online marketing and advertising campaigns.
Data silos are isolating by nature. Data lakes overcome this limitation by combining datasets from different departments under one roof. The consolidation gives data science engineers a bird’s-eye view to find and prepare relevant data for analytics. When duplicate data platforms are taken out of an enterprise, IT and data management costs go down.
Below are a few of the several benefits that data lakes offer:
- Centralization of various data sources ensures quick adaptability to changes.
- Users from different departments have flexible access to unlimited data types.
- It empowers users and data scientists to create queries, data models, and analytics applications on the fly.
- The technology involved in making the Data Lake architecture is mostly open-source and configured on low-cost hardware. To name a couple, Spark and Hadoop make Data Lakes relatively inexpensive to implement.
- Machine learning, statistical analysis, predictive modeling, text mining, SQL querying, and many more analytics methods empower Data Lakes.
- The long-term cost of ownership reduces due to the economical storage of data files.
What challenges does a data lake bring?
Despite having the edge over data warehouses, data lakes lack some critical features. Inefficient performance optimizations, neglected data quality, and a lack of transaction support have converted most enterprise data lakes into data swamps.
- Unreliable: Data reliability issues escalate without the necessary tools in place. Due to this, data scientists find it challenging to analyze the data. In addition, such problems can result from combining streaming data with batch or corruption factors.
- Complex: A data lake’s enormous volumes of data often throw seasoned data engineers and scientists off-balance. It requires specialized and professional skills to analyze data from data lakes.
- Sluggish: Traditional query engines become slower as the data lake increases in size. Improper data partitioning and metadata management are two bottlenecks slowing conventional queries.
- Data Quality Concerns: Maintaining the data integrity of data lakes is difficult because filtering the data is a time-consuming process. If data that can’t be used is left unorganized and doesn’t have clear metadata tags or identifiers, data lakes can quickly become too much for us to handle.
- Security Concerns: Poor data visibility makes it difficult to govern and secure data lakes. Additionally, we can’t update or delete data from it, which further limits security features. Without proper supervision, sensitive data may slip into data lakes and become available to anyone with access to the data lake.
So, as we see, a traditional data lake architecture is insufficient to meet the requirements of modern enterprises. Due to this, organizations adopt complex architectures where data is stored across multiple storage facilities such as databases, warehouses, and other storage systems. However, enterprises that aspire to harness the potential of data analytics and machine learning in the future should work toward unifying all the data in a data lake.
What are the types of Data Lakes?
Data Lakes are primarily implemented in two ways:
Cloud Data Lakes: In this type, the hardware and software needed to implement the Data Lake are in the cloud. Enterprises subscribe to the service through a pay-as-you-go model. The model is easily scalable, and the service provider takes care of reliability, security, performance, and data backup. So, the users can focus in entirety on what data to include and how to analyze their Data Lake.
Built-in features like cloud-native security, integration metadata, end-to-end data lineage, and data protection services make Data Lake cloud services quite attractive. The providers may also help enterprise IT teams with impact or root cause analysis and compliance.
On-premises Data Lakes: Installing and configuring software and hardware to run Data Lake on servers and storage within a company’s data center. The initial investment to buy hardware and software licenses is required. Besides, IT expertise to configure, operate, and manage the data lake is needed. On-premises data lakes offer superior performance to users located within the business facilities.
In-house software engineers also have to deal with downtime and outages apart from orchestrating batch ETL jobs and integrating various tools to consume, process, and analyze the data stored.
Data Lakes Architecture
As the volume of data continues to rise, the architecture of the Data Lake is constantly evolving to keep up with the ever-increasing demands. However, even if the software used and implementation process vary, data lakes do share a few architectural features in common.
Data Ingestion Layer: An array of prebuilt connectors or relational databases brings data from various sources into the Data Lake. Data ingestion is not only limited to real-time inputs like financial transactions, web clickstreams, sensor data feeds, or social media inputs but also through batch processes.
Data Security: Data lakes are vital to protect as they can soon become an enterprise’s most significant and vulnerable business data. User authentication is crucial to ensure only an authorized view of the data.
Data Catalog: The Data Lake catalog enables users to precisely identify the information they are looking for. The metadata within a record describes each dataset by providing information about its quality, source, and type.
Data Processing and Analytics: Business analysts and data scientists explore the data using analytical tools and gain valuable insight from data lakes. Enterprise IT teams use business intelligence, machine learning, and statistical analysis tools. Their choice of tool is based on their expertise and needs.
What are Data Warehouses?
Data warehouses are data management systems containing heaps of historical data designed to perform queries and analyses. Data is ingested from different sources, such as transactional applications and application log files.
Enterprises leverage analytical capabilities to extract valuable insights from data warehouses and improve decision-making. Over time, an enterprise’s variety and volume of data become vital to business analysts and data scientists.
A traditional data warehouse often includes the following:
- Relational database to store data
- An extraction, loading, and transformation (ELT) process for analysis
- Data mining, reporting, and statistical analysis potential
- Analytical tools to present and visualize the data
Data Lake vs. Data Warehouse
Although the data lake and the data warehouse store enormous amounts of data, they are not like two peas in a pod. While a data warehouse is a storehouse of filtered and structured data processed for a specific purpose, a “data lake” is a massive pool of unrelated and unstructured data.
| Data Lake | Data Warehouse | |
| Data structure | Data is in its native and unprocessed form | Stores processed and refined data |
| Purpose | No clear goal | Pre-determined |
| Size | Much larger storage capacity | Planned data needs lesser space |
| Users | Data scientists | Business intelligence professionals |
| Accessibility | Easily updatable and highly accessible | Complex and costly to update |
Data warehouses save money on storage by not maintaining unused data. On the contrary, data lakes risk turning into data swamps if left unattended. However, data lakes don’t have a fixed architecture and are therefore easy to update. Therefore, any changes made are quickly visible on a data lake.
FAQs:
1. What is the primary purpose of the data lake?
The primary purpose is to provide detailed data for data exploration and analytics.
2. What is “Data Lake” storage?
A data lake is a repository that stores raw data from multiple sources in an open format.
3. How do you access data from the data lake?
Data is accessed using catalog and metadata information embedded with each data entry.
4. What’s the difference between a data lake and a database?
A database stores current data to run an application, whereas a data lake stores current and historical data to aid data analysis.
