Data Warehouse Software: Definition, Tools & Architecture

Develop a comprehensive understanding of the data warehouse to strategically position your organization for data-driven success.

What is data warehouse software?

Data warehouse software is a set of tools and technologies that enable the creation, management, and utilization of a data warehouse – a central repository of integrated data from one or more disparate sources, used for reporting and analysis.

Realizing the full benefits of your data requires effective data warehouse management, which can be incredibly challenging without the right tools.

Data warehouse software automates many tedious and error-prone tasks (e.g., ETL) and plays a crucial role in streamlining complex processes.

A data warehouse (DWH) provides the foundation for business intelligence (BI), but the software unlocks the potential.

Businesses can reliably use DWH tools to visually design data pipelines, schedule data updates, and monitor performance.

The result? More time and resources dedicated to extracting data insights, rather than wrestling with the infrastructure.

Benefits of Data Warehouse Software


Automated Data Ingestion
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Centralized Data Access
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Unified Analytics for Enhanced Insights
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Accelerated Query Response Times
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Simplified Data Management
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Seamless Integrations with Business Tools
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High Quality Data for Business Intelligence
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Control-M by BMC Software

Centralize orchestration of popular data warehouse solutions such as Snowflake, Databricks, AWS, and Google Cloud Platform (GCP). Automate your entire data pipeline from ingestion to analysis.

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How does data warehousing software work?

Ingest & Refine

Data is ingested from a variety of sources (e.g., sales platforms, CRMs, databases). Raw data undergoes cleaning, standardization, and transformation so it can be optimized for analytical use.

Organize & Structure

Refined data is indexed within the data warehouse. This involves data loading and creating models that define the relationships between different pieces of information.

Explore & Discover

Users can explore and generate targeted insights, often within the data warehouse platform itself. User-friendly interfaces can help all team members uncover trends, patterns, and opportunities hidden within the data.


BMC Control-M Data Warehouse Integrations


Snowflake
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DataBricks
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AWS
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GCP
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ING Bank consolidates big data management with a two-pronged solution

Big Data Pipeline Monitoring

Data Warehouse Architecture


Row-Based Data Warehouse
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Column-Based Data Warehouse
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On-Premise Data Warehouse
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Cloud-Based Data Warehouse
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Key Components of Modern Data Warehouse Tools

The best DWH tools possess most or all of the following features.

Intuitive Interface

A well-designed interface empowers users to directly access, explore, and visualize data, reducing their dependence on IT and accelerating the discovery of actionable insights.

Low-Code/No-Code Capabilities

Self-service analytics tools further democratize data access, enabling a broader range of users to perform analysis without specialized technical expertise.

Automated Data Pipelines

Automation streamlines the flow of information into the warehouse and frees up valuable time and resources, allowing teams to focus on analysis and interpretation rather than manual data wrangling.

Scalability and Adaptability

The ability to dynamically scale resources to meet fluctuating data volumes and user demand is crucial for maintaining performance and avoiding bottlenecks.

Versatile Data and BI Connectivity

Seamless integration with a wide array of data sources and BI tools creates a more holistic view. Pre-built connectors and APIs can simplify and accelerate these integrations.

Powerful Data Querying and Analysis

Features like standard SQL support, massively parallel processing (MPP) architecture, and query optimization engines ensure efficient access to data, enabling users to quickly extract meaningful insights.

Data Security and Governance

Encryption, access controls, data masking, and audit trails are essential for maintaining data integrity, complying with regulations, and building trust among stakeholders. Data governance frameworks ensure data quality, consistency, and compliance.

Flexible Deployment Options

The right cloud, on-premise, or hybrid deployment aligns with your specific business needs, regulatory requirements, and IT strategies. This also helps organizations optimize cost, performance, and data sovereignty.

Additional Building Blocks of Data Warehouse Software


Object Storage
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Warehousing Tools
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Performance Tools
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Resource & Workload Management
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Data Modeling
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Frequently Asked Questions


Is SQL a data warehouse tool?
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What are the three data warehouse models?
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What do data warehouse solutions cost?
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What does the implementation process look like for data warehouse products?
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How is DWaaS changing the data warehousing landscape?
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Can data warehouse management software automate data warehousing processes?
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What are the biggest challenges associated with data warehousing solutions?
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