Data Lake vs Data Warehouse: A Comparative Analysis

Data lake vs Data warehouse, these days, has become a closely monitored and heavily debated contest in the digital transformation domain. There was a time when organizations across the globe were extremely reliant on and satisfied with data warehouses for satiating their data storage needs. However, things in this regard have witnessed a significant change. A huge volume of raw and diverse data has now made the creation of data lakes a mandate. Read on to know about the fundamentals, characteristics, and capabilities of data warehouses and data lakes. The major differences between these two carriers of data are also documented in detail. 

Data Warehouse: An Overview 

A data warehouse is a traditional data storage system developed to store and process data structured according to predefined schemas and metrics. This data is used for analysis and reporting. Firms also utilize this data for making rational decisions based on historical and current data. The ability of a data warehouse to store historical data in files and folders makes trend analysis easy. The acquisition of precious insights from the centrally stored data helps organizations to serve their customers as per their expectations and specific requirements. 

Apache Hive is one of the most popular tools that is being used for data warehousing. Hive facilitates data processing by easily integrating with several Hadoop components like HBase and Spark. Hive makes data analysis on Hadoop easy through its SQL-like interface. Hive is equipped with query execution plans and indexing capabilities to improve query performance. 

Data Lake: The Basics 

A data lake is a centralized repository capable of storing structured, semi-structured and unstructured data regardless of its scale. A data lake owes its flexibility and cost-effectiveness to its schema-on-read approach, an effective data management strategy that involves the interpretation of data only when it is being read or queried. The quality of data stored in a data lake can only be maintained through flawless management and governance by the administrators. Horizontally, a data lake is highly scalable. 

The creation of an effective data lake involves the use of several tools and technologies. Hadoop Distributed File System (HDFS) is a java-based storage system that facilitates parallel processing by distributing data and dividing massive files into blocks that are easily stored in the cluster. HDFS features a master-slave architecture that has NameNode and DataNode as its key components. NameNode is responsible for managing the metadata. DataNode relies on the instructions from the NameNode for reading or writing the data blocks. 

Apache Mahout and Tensor are used to create machine learning models on the stored data. Technologies like Apache Hudi and Delta Lake are often used for managing data processing workflows to ensure that the quality and reliability of data can be maintained. 

Data Lake Architecture 

Data lake architecture refers to a framework used for storing large volumes of data. Data lakes feature the following components: 

Data Ingestion Layer 

Data lakes allow the ingestion of data either in batches or in real-time. Data is collected from several connectors and then inserted in the data lake. 

Data Storage Layer 

Data storage is the primary responsibility of a data lake. Hadoop Distributed File System (HDFS) is one of the most popular systems used for storing data. 

Data Distillation Layer 

This layer intends to facilitate data analysis by converting raw data into structured data. 

Data Insights Layer 

This layer facilitates data exploration and discovery. The extraction of the most relevant insights from the data lake is the primary responsibility of data scientists. 

Data Lakes and Data Warehouses: Use Cases 

Data lakes are extremely fruitful for a rich number of industries. Data lakes can be effectively implemented in the following scenarios: 

Reporting 

Data lakes are widely used for generating reports because of their ability to provide extremely reliable data. 

Advanced Analytics 

Data lakes simplify data processing and make it possible for the users to explore unfiltered data and create the desired queries. 

Big Data Processing 

Data lakes can handle and process huge volumes of data. This capability makes data lakes facilitators of parallel processing and distributed computing. 

Machine Learning 

The ability of data lakes to process unstructured and semi-structured data like images and videos makes them essential for enhancing the machine learning experience. 

Similarly, data warehouses are also utilized by a good number of industries. The following tasks can easily be performed with the help of data warehouses: 

Performance Evaluation 

Data warehouses facilitate performance evaluation by offering a centralized view of the performance metrics. The managers can use these metrics to bridge the gap between expectations and the actual output. 

Marketing Campaigns 

Data Warehouses play a vital role in ensuring the success of marketing campaigns by serving the organizations with structured and consolidated data. This data helps in acquiring insights pertaining to market trends and consumer behavior. 

Data Warehouse and Data Lake: What Differentiates Them?  

Data warehouses as well as data lakes are used to store data, however, the following characteristics make them considerably different: Let's understand how data lake services and data warehouse services are different from each other.

Adaptability 

A data warehouse involves a lot of effort and time to adapt to changes, unlike a data lake which is highly dynamic and easily adapts as per the requirements. 

Implementation 

Easy implementation is one of the key features of a data warehouse owing to the storage of structured data, however, the storage of multiple data types in a data lake results in complex implementation. 

Cost 

The flexible storage ability of a data lake makes it more cost-effective than a data warehouse which requires massive operational costs. 

Data Transformation 

A data warehouse requires all the desired changes to be made before loading the data. A data lake, on the other hand, gives the users the freedom to store raw data and make the changes whenever necessary. 

Approach 

A data warehouse uses batch processing for structured data, whereas a data lake can use batch as well as real-time processing as per the requirements. 

Organization 

A data warehouse features the organization of data in tables bundled with a relational structure. A data lake is equipped with a hierarchical storage system that features raw and processed zones, so the flexibility in the storage architecture is high. 

Data lakes are primarily used by data scientists and data engineers, whereas data warehouses are used by business analysts and data warehouse professionals. Big Data, IoT, social media, and streaming data are the major sources of a data lake. On the other hand, application, business, transactional data, and batch reporting are the key sources of a data warehouse. 

Hexaview Technologies is a digital transformation organization equipped with rich expertise and over a decade of experience in data science, data management, and data integration. It has worked with an impressive number of clients on data lake creation projects. Hexaview has developed many data-driven solutions and has also succeeded in deriving priceless insights from the huge volumes of data. Despite offering superlative services, Hexaview always persists with a reasonable pricing strategy. Delighting all its clients is the primary objective of Hexaview. 

Hexaview facilitates the decision-making process of its clients by offering fully managed and automated big data services, Business Modeling, BI Consulting, BI Implementation, and BI Migration services. Data Mining and Data Engineering services are also provided by Hexaview to ensure that the value of data does not decline at all.