Data warehousing is currently experiencing a phase of rapid growth and development due to the enormous availability of data worldwide. Interestingly, one Study by Domothat 2.5 quintillion bytes of data are generated every day, which will grow to 463 exabytes in 2027 and 463 exabytes in 2025. Data warehousing and data lakes are essential for handling critical business decisions and data optimisation. They help maintain customer satisfaction by providing comprehensive insights. They serve as important sources for staying competitive and responding to market needs. Some of the leading companies expanding the data warehousing sector include Microsoft, Oracle and Google, followed by SAP, IBM, etc.
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What is a data warehouse?
A data warehouse is a Data management system that collects, integrates and organises historical data from various resources within an organisation. It summarises this data in a structured format to provide a unified view for analytical processing. It supports Business Intelligence-(BI) activities and Data mining through Data analysis and machine learning.
The analytical processing functions of data warehouses enable companies to gain and provide profitable insights to improve decision-making. They help to create an invaluable data history for Data ScientistsML experts and business analysts.
In contrast to conventional data warehouses Cloud data warehouses scalable compute and storage resources, dynamic workload isolation and a consumption-based cost model. They enable real-time analytics with fast retrieval of streaming data and offer advanced security features for data confidentiality and regulatory compliance.
A comprehensive look at business intelligence: how companies can make informed decisions and react quickly to market dynamics by analysing and visually processing data.
Data warehouse vs. data lake
Data warehouses and Data Lakes are repositories for storing large amounts of data from various sources. Both support analytical processes and enable companies to gain valuable insights from the stored data. Data to win. Although data warehouses and data lakes have similarities, they are fundamentally different technologies.
| Criterion | Data Warehouse | Data Lake |
|---|---|---|
| Structure of the data | Structured data that is filtered and processed | Structured, semi-structured and unstructured data in its original raw form |
| Purpose | for current operational purposes and predefined business requirements | explorative analyses and experiments, as they can handle different data formats flexibly |
| Analysis | concentrates more on Data visualisation, business intelligence and data analysis | supports machine learning, predictive analyses, BI, visualisations and Big data analyses |
| Schemes | are defined before the data is stored to ensure data consistency and data usage across the organisation | defined after data storage for fast data acquisition |
| Processing | the ETL approach (Extract, Transform and Load) is used to first transform data before it is loaded into the warehouse | the ELT approach (extract, load, transform) is used to load data before transformation |
| User | Business professionals and managers who use data for analytical operations | Data scientists, data analysts and Data Engineers |
| Costs | expensive compared to a data lake | Cost-effective storage and processing |
Find out how data lakes serve as central collection points for huge and diversified data volumes and enable efficient big data analytics.
Basics, use cases and benefits of a data lake: Everything companies need to know about data lakes
Data warehouse vs. database
In general, databases and data warehouses collect and store data to perform queries in order to achieve business goals. However, the difference lies in the mechanisms, processing, accessibility and flexibility of the technologies.
| Criterion | Data Warehouse | Database |
|---|---|---|
| Structure of the data | organises data in tables with facts and dimensions for analytical queries | organises data in tables with relationships for transactional processing |
| Purpose | Designed for analytical processing, reporting and historical data analysis | designed for transaction processing and real-time data management |
| Data volume and latency | Processes large amounts of historical data with periodic updates and batch processing | processes comparatively small amounts of data with real-time queries |
| Use cases | Business intelligence, data analysis, healthcare, financial services, e-commerce, retail, marketing, etc. | for daily operations, e-commerce, websites, retail, telephone companies, etc. |
| Query behaviour and complexity | Supports complex analytical queries on large data sets for data analysis and reporting | Efficient processing of simple and complex queries for smaller data sets |
| Costs | Expensive, especially for extensive analysis tasks | more cost-effective for transactional workloads |
Components of a data warehouse
Data Warehouse is a comprehensive technology that builds and stores historical data for strategic decisions. It is set up in specific phases that contribute to achieving optimal, data-optimised solutions. The most important components of a data warehouse architecture include
- Data sourcesThe data in a data warehouse comes from the company's operational systems and various external sources. These data sources can include various operational systems such as transaction databases, ERP systems, CRM platforms, spreadsheets and external data feeds.
- ETLETL processes are responsible for extracting data from various sources. They convert the data into a standardised format suitable for analysis and load it into the data warehouse. ETL is a critical component of a data warehouse as it helps to maintain the accuracy, integrity and coherence of the data within the warehouse.
- Central databaseThe data warehouse database is the central repository in which the data is stored in a structured manner and which is optimised for analytical queries and reports. It consists of fact tables, in which transaction data is stored, and dimension tables, which provide context and dimensions for analysis.
- MetadataThey are data about data and an important component in a data warehouse. Metadata specifies information about the structure, source, transformation rules and origin of the data within the warehouse. It helps with data management, tracking the data sequence and optimising queries by providing users with content to understand the data.
- Data warehouse access toolsThese tools are software applications or interfaces that are used by end users to interact with and query the data stored in the data warehouse. Examples include BI platforms, data mining and application development tools and OLAP tools.
- Data Mart (optional): A data mart is a subset of data warehouses that meets the requirements of different business areas, departments or user groups. It can be set up independently or derived from the centralised data warehouse.
The data warehouse architecture
A data warehouse is a organisational system used for the analysis and reporting of structured and semi-structured datathat originate from various sources, e.g. point-of-sale transactions, marketing automation, CRM and beyond. However, there are limitations when processing unstructured or real-time data. Unstructured data has no predefined data model, which poses challenges for storing, retrieving and analysing it in traditional data warehouse architectures that are tailored to structured data.
The data warehouse architecture consists of components that are divided into three levels. These include
- Lower levelThis level consists of a data warehouse server, usually a relational database system, which collects, analyses, filters and transforms data from various sources using ETL or ELT, depending on the requirements. The lower level is essential for data convergence and meaningful information analysis.
- Middle levelOnce the data has been analysed, the crucial phase of OLAP (Online Analytical Processing) comes into play. OLAP enables queries to be answered quickly using various OLAP models, including ROLAP (Relational OLAP), MOLAP (Multidimensional OLAP) and Hybrid OLAP, and makes query processing and analysis robust and efficient.
- Top levelThis level represents the front-end user interface of the data warehouse through BI tools that allow users to perform ad-hoc analyses on big data. The tools also help with data filtering, statistical analysis, reporting and data mining for data-driven solutions.
In our Top 10 article, we introduce you to other BI tools that can help you gain valuable insights from data.
Advantages of a data warehouse for companies
Data warehousing is of great benefit to companies and organisations, as it enables their Increased efficiency and transparency and improved risk forecasting and decision-making. These decisions provide actionable insights that lead to an increase in ROI (return on investment). Although the provision of a data warehouse involves initial investment costs, the benefits and added value it brings outweigh these costs.
The Cost structure and potential investment volume for a data warehouse depend on various factors. These include the chosen deployment model (on site or in the Cloud), the volume and complexity of the data, the required features and functionalities, and ongoing maintenance and support. Unlike on-premise data warehouses, cloud data warehouses offer flexibility and cost efficiency, making them an attractive option for organisations of all sizes.
The most important advantages of using data warehouses include
- Standardisation of dataData warehousing consolidates the collected data in a consistent, standardised format and thus ensures its reliability for business requirements.
- Improved business intelligenceIt also bridges the gaps between rich raw data and curated data, providing insights and integrating them to serve complex business queries for better understanding.
- Data optimisation and workloadsData warehouses reduce the time it takes to effortlessly connect to data analysis and analysis tools. Business intelligence tools and thus reduce labour costs and time.
- Improved decision-makingThis also enables the assessment of risks and the identification of outliers and requirements while providing flexible, solution-orientated services.
Data wrangling harmonises a wide variety of data types in order to improve analysis quality and consistency. In our articles, we shed light on this important process in data preparation:
Examples of data warehouse solutions
Data warehousing is crucial to achieving business goals. Leading technology companies such as Snowflake, Google BigQuery, Amazon Redshift, Microsoft's Azure Synapse and IBM DB2 offer robust solutions. These platforms are characterised by query processing, serverless options, analytics and scalability.
The data warehouse solutions available on the market are as follows:
- Amazon RedshiftAmazon Redshift is a cost-effective, fully managed data warehouse service in the cloud. It enables enterprise-level data analytics and offers fast query performance and scalability. Amazon Redshift has features such as automatic infrastructure provisioning, fast data processing and versatile data queries.
- Google BigQueryGoogle BigQuery is a serverless, highly scalable data warehouse solution. It enables real-time analyses with standard SQL and advanced analyses with ML models. Google BigQuery provides automatic scaling and robust security for data-driven organisations.
- SnowflakeSnowflake is a cloud-based data warehouse platform that provides elasticity, unlimited concurrency and secure data sharing. It supports multiple data types, including structured, semi-structured and unstructured data. It also has integrated functions for Data governance, Security and compliance.
- Azure SynapseMicrosoft Azure Synapse Analytics is an analytics service that combines data integration, enterprise data warehousing and big data analyses. It enables users to analyse large volumes of data with high performance and scalability while integrating with other Azure services and tools.
- IBM Db2 WarehouseIBM Db2 is a fully managed, cloud-based data warehouse solution designed for high-performance analytics and data warehousing workloads. It offers advanced features such as in-memory processing, workload management and data compression.
Data pre-processing, a crucial step in data processing, prepares data in a similar way to oil in order to maximise its value for business decisions.
Increase business potential with cloud data warehouses
Modern data warehouses, especially those in the cloud, play a crucial role in organisations' digital transformation efforts. These data warehouses provide a comprehensive perspective that is essential for strategic decision-making by combining data from various internal systems and external sources. While the initial investment in a data warehouse is significant, the benefits of centralised data management, improved business intelligence and scalability outweigh the costs. With these advances, organisations can use data to unlock new opportunities, drive innovation and remain competitive in dynamic market environments.
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