Key Differences Between Data Warehouse and Big Data
The world is a whole of data, and new data is constantly being created. What to do with this data is often a quantitative question. Our glasses are too small, so we must look online for bigger and better ones.
The term “big data” describes only the liquid, while terms like “database” or “data warehouse” refer to a cup full of data. While these terms are often used together, as in this article, it is essential to recognize that big data and data warehouses are very different. When handling large amounts of data, the former is a counter or toolkit, and the latter is a separate tool (often used as part of that toolkit). However, as we will show in this article, data warehouse and big data usually perform similar tasks, gathering similar amounts of different information and producing reports or analyses based on that information.
Key differences between data warehouses and big data.
Meaning
Data Warehouse
Data warehouses are not so much technologies as architectures. They allow you to create analytical reports and extract data from various SQL-based data sources, usually relational databases. A data warehouse is a collection of analytical reports made by a single process: the data warehouse.
Big Data
Big Data is a technology based on the volume, velocity, and variety of data. Diversity refers to the number of different data types (typically, all data formats are supported), volume refers to the amount of data from other sources, and velocity refers to the speed at which data is processed.
Data Sources
Data Warehouse
A data source consists of one or more relational databases, well-structured but potentially heterogeneous data sets. Although the relational databases that feed the data warehouse are structured, the data can be complex.
Big Data
Input data are open and accept any value. Users, automated logs, and other forms of rapid data collection are often used as sources. Data does not need to be structured.
Goals And Design
Data Warehouse
Data warehouses are architectural structures designed to simplify the integration, organization, and retrieval of historical data for reporting and business intelligence purposes. They are characterized by a structured architecture that enables efficient search and analysis. In addition to technology, data warehouses also include data modeling and ETL (extraction, transformation, and loading) processes to ensure data consistency and high quality.
Big Data
The main goal of big data technology is to handle large amounts of data with scalable processing and storage. Shared hardware clusters, distributed file systems, and parallel processing are standard architectural features of extensive data systems. These systems are ideal for real-time computing, machine learning, and data exploration, as they are designed to handle large amounts of data.
Contexts
Data Warehouse
Organizations often use data warehouses when they want to know how to make informed decisions (e.g., what’s happening in the business, planning for next year based on current year performance data, etc.) These types of reports require reliable data from various sources.
Big Data
All sources, including social media, financial transactions, sensor or machine data, are accepted. The latter may, but need not necessarily, be derived from DBMS products.
Processing Method
Data Warehouse
Data warehouses typically do not use distributed file systems to process data. They use structured query language (SQL) to analyze and retrieve data. Data warehouse systems are ideal for reporting and analysis because they are designed to handle complex SQL queries.
Big Data
Big Data systems use parallel processing techniques and distributed file systems to analyze and process large amounts of data. They are suitable for various computational tasks designed for batch and real-time processing.
Memory
Data Warehouse
Database changes do not replace data created or stored in a data warehouse. Therefore, data warehouses are “non-volatile” storage systems.
Big Data
In addition, legacy data is still being replaced by extensive data systems, which use flexible big data warehouses to store historical data, even without timestamps.
The Impact of Data Revision
Data Warehouse
Data warehouses need to be more flexible when implementing data updates. Careful data integration procedures, such as ETL pipelines, must ensure that updates are adequately integrated without compromising existing data structures.
Big Data
When new data is added, or changes are made to Big Data systems, they are often recorded as files or events. These changes can be managed independently and do not directly impact existing data.
Subject Oriented
Data Warehouse
A data warehouse is object-oriented because it provides information about a specific object (e.g., product, customer base, suppliers, sales, revenue, etc.) It does not deal with day-to-day operations. Its primary purpose is to support decision-making by analyzing and presenting data.
Big Data
The main difference between object-oriented data and big data is the source of the data. Big data can extract and process all data types, including social media, sensors, and machine data from specialists. In addition, the main goal of big data is to enable accurate data analysis.
Languages For Applications
Data Warehouse
The most common way to retrieve and modify data from a data warehouse is through SQL queries. Because data warehouse platforms provide robust SQL support, analysts and business users can easily interact with data using familiar query language techniques.
Big Data
Extensive data systems use specialized languages and tools to analyze and process data. They typically use query languages specific to the data processing system, such as Pig Latin for Apache Pig and HiveQL for Hive.
To Summarize
Now that you know the differences between big data and data warehouses, you can decide which system is best for you and your organization. A data warehouse provides visibility into historical data for business analysis, is suitable for queries and reports, and is ideal for structured data. However, Big Data platforms can easily handle large amounts of structured and unstructured data. They enable real-time data processing, advanced analytics, and information extraction from various data sources.