Data Warehousing

Data and analytics have become crucial to businesses to stay competitive. Business users rely on reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. Data warehouses power these reports, dashboards, and analytics tools by storing data efficiently to minimize the input and output (I/O) of data and deliver query results quickly to hundreds and thousands of users concurrently.

Enterprise data warehouses enable organizations to share petabytes of data across thousands of users with the security, governance, and availability large organizations demand.

What is Data Warehousing

Data warehousing is the process of centralizing an organization's vast data collections from dispersed data sources inside an enterprise data warehouse to support the organization's data-driven initiatives with on-demand data access.

Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications.

What is a Data Warehouse?

A data warehouse is a federated repository (uses a unifying standard for separate databases) for all the data collected by an enterprise's various operational systems.

A Data warehouse is typically used to connect and analyze business data from heterogeneous sources. The data warehouse is the core of the BI system which is built for data analysis and reporting. It is a blend of technologies and components which aids the strategic use of data.

Business Initiatives Supported by Data Warehousing

By storing all key data in and making data available from a central data warehouse, organizations can support many of their critical, ongoing processes and digital transformation initiatives. Just a few of these include:

• 360 degree BI & analytics - mass data analysis
• Historical data mining
• Data governance and regulatory compliance
• Data backup, storage and archiving
• Custom enterprise app development
• QA & testing
• AI & ML initiatives
• Digital Transformation and broader innovation

Benefits of Data Warehousing and Unified Data Access

The benefits of unified data access - and data warehousing, by extension - are enormous and will only grow more valuable as organizations attempt to tread water amid the rising tidal wave of data proliferation across hundreds of enterprise applications.

The underlying goal of data warehousing is to provide desperately needed, unified data access at scale to your organization's key decision-makers, analysts, developers, and data-consuming applications. You can take many paths to achieve this end, from data warehousing to live data integration, data virtualization, and others. In this guide, we cover just some of the many benefits of unified data access, with a focus on data warehousing, but in truth, these advantages apply to most methods of achieving unified data availability.

More Consistency Across Your Data

A properly deployed data warehouse makes it possible to convert data from many different sources into a unified format. This allows you to standardize processes across the organization, while providing analysts all the data they require.

360 Degree Analysis

With data unified in one place, (and in the same format), analysts can perform more comprehensive, 360 degree reviews. Organizations can grasp both a deeper and broader understanding of all the trends happening throughout their companies.

Better Organizational Alignment

With better 360 degree insights and different teams (sales, marketing, operations, support, etc.) all using the same repository for reporting, each department can better align with the others. All the key stakeholders can see what's happening throughout the organization and stay on the same page.

Scalable Performance

Data warehouses are built specifically for analysis and transactional retrieval, rather than just the simple upkeep of individual records. As a result, they offer both a more scalable solution for storing large volumes of data and faster processing for advanced data analysis, processing queries from various BI tools to enable more efficient analysis.

Simplified API Management

With data warehouses, there's no need to juggle or manage separate APIs for data access in every single application or database. Instead, simply copy the data from a given data source and replicate it into a data warehouse to save time and reduce errors.

No API Limitations

One benefit of using data warehousing in particular, as compared with other methods of data connectivity, is avoiding costly, complex API limitations. Data warehouses expand your data access by allowing you to circumvent slow APIs and query limits some API providers enforce. Instead of continuously running queries against the APIs to pull data, you can schedule larger data replications into your data warehouse with an enterprise data pipeline solution.

Improved Decision-Makaing Process

Data warehousing provides better insights to decision makers by maintaining a cohesive database of current and historical data. By transforming data into purposeful information, decision makers can perform more functional, precise, and reliable analysis and create more useful reports with ease.

Speed and Self-Service Enablement

A data warehouse standardizes, preserves, and stores data from distinct sources, aiding the consolidation and integration of all the data. Since critical data is available to all users, it allows them to make informed decisions on key aspects. In addition, executives can query the data themselves with little to no IT support, reducing total turnaround time for analysis and reporting while saving more time and money.

Higher SEcurity: Consolidated Data Access

By using a data warehouse as a unified data repository, you can not only bring together all your data, you can make it more secure through consolidated data access and consistent, stringing authentication standards.It's also worth mentioning that data warehouse allows users to access critical data from the number of sources in a single place.

How Data Warehousing Works

Typically, businesses use a combination of a database, a data lake, and a data warehouse to store and analyze data. A Data Warehouse works as a central repository where information arrives from one or more data sources. Data flows into a data warehouse from the transactional system and other relational databases. The data is processed, transformed, and ingested so that users can access the processed data in the Data Warehouse through Business Intelligence tools, SQL clients, and spreadsheets.

A data warehouse merges information coming from different sources into one comprehensive database.

Types of Data Warehouses and Their Common Use Cases

Enteprprise Data Warehouse (EDW)

An enterprise data warehouse (EDW) is a database, or collection of databases, that centralizes a business's information from multiple sources and applications, and makes it available for analytics and use across the organization. EDWs can be housed in an on-premise server or in the cloud. The data stored in this type of digital warehouse can be one of a business's most valuable assets, as it represents much of what is known about the business, its employees, its customers, and more.

Operational Data Store

An operational data store (ODS) is a central database that provides a snapshot of the latest data from multiple transactional systems for operational reporting. It enables organizations to combine data in its original format from various sources into a single destination to make it available for business reporting. In an ODS, the data warehouse is refreshed in real time. Hence, it is widely preferred for routine activities like storing employee records.

Data Mart

A data mart is a subset of data stored within the overall data warehouse, for the needs of a specific team, section or department within the business enterprise. For example, a central archive will hold data for the entire business organization, while a data mart makes specific subsets of data available to a set group of users so they don't have to waste precious time searching the central archive for the data they need. Data marts make it much easier for individual departments to access key data insights more quickly and helps prevent departments within the business organization from interfering with each other's data.

Logical Data Warehouses and Data Virtualization

In many cases, it's more efficient and less resource-intensive to provide a virtualized version of your data, rather than actually replicating or loading your enterprise data to a central data warehouse. In this implementation of data warehousing, users can access data that resides across dozens or even hundreds of data sources and databases as if it's all located inside one database, no matter where the data is housed.

The logical data warehouse provides a virtual data layer that makes the data look like it resides inside a common database, with a common interface and standardized data model. Many organizations employ logical data warehouses to augment and extend their existing data warehouse systems with data from non-standard sources, such as Hadoop or NoSQL databases. These virtualized data systems offer tremendous flexibility to publish the augmented information in many different formats to suit a range of business users and applications.

For more resources on logical data warehousing and data virtualization:

• Watch our Webinar on embedded data virtualization
• Or, explore the CData Query Federation Driver

Data Access Configurations

Stationary Data Warehouses

In this type of data warehouse, the end use is given direct access to the data inside the data warehouse. For many organizations, infrequent access, volume issues, or corporate necessities dictate such an approach.

Multi-Stage Data Warehousing

This refers to using multiple stages in transforming and storing data. Data is first extracted from various data sources, then grouped and placed in designated staging areas. Next, the data is transformed to fit a central data warehouse before being placed into specific operational databases where specific teams, departments, and users will access, analyze and build on the data. This configuration is suitable for organizations where end users need both a central, summarized repository of all data for long-term storage and historical analysis as well as information for up-to-the-minute reporting and live applications.

Such a warehouse will need highly specialized and sophisticated 'middleware,' possibly with a single interaction with the client. This may also be essential for a facility to display the extracted record for the user before report generation. An integrated metadata repository becomes an absolute essential under these circumstances.

A data pipeline or ETL/ELT tool can serve as this middleware pipeline and easily handle the loading, replication and transformation of data through the various steps of the process to provide data access usable by anyone, in any way, across the entire organization.

Most Popular Data Warehousing Platforms

A data warehouse is a critical database for supporting data analysis and acts as a conduit between analytical tools and operational data stores. The most popular data warehousing solutions include a range of useful features for data management and consolidation. You can use them to extract/curate data from a range of environments, transform data and remove duplicates, and ensure consistency in your analytics.

Google BigQuery

BigQuery is a cost-effective data warehousing tool with built-in machine learning capabilities. You can integrate it with Cloud ML and TensorFlow to create powerful AI models. It can also execute queries on petabytes of data for real-time analytics. This scalable and serverless cloud data warehouse is ideal for companies that want to keep costs low. If you need a quick way to make informed decisions through data analysis, BigQuery has you covered.

AWS Redshift

Redshift is a cloud-based data warehousing tool for enterprises. The platform can process petabytes of data quite fast. That's why it's suitable for high-speed data analytics. It also supports automatic concurrency scaling. The automation increases or decreases query processing resources to match workload demand. Although tooling provided by Amazon reduces the need to have a database administrator full time, it does not eliminate the need for one. Amazon Redshift is known to have issues with handling storage efficiently in an environment prone to frequent deletes.

Snowflake

Snowflake is a data warehousing solution that offers a variety of options for public cloud technology. With Snowflake, you can make your business more data-driven.You may use Snowflake to set up an enterprise-grade cloud data warehouse. With Snowflake, you can analyze data from various unstructured and structured sources. However, Snowflake is dependent on Azure, AWS, GCS. The support can be a problem whenever one of those cloud servers has an independent outage.

Microsoft Azure Synapse

Microsoft Azure is a robust platform for data management, analytics, integration, and more, with solutions spanning AI, blockchain, and more than a dozen unique databases for varying use cases. Among them is Azure Synapse, formerly known as Azure SQL Data Warehouse, a platform built for analytics, providing you the ability to query data using either serverless or provisioned resources - at scale. Azure Synapse brings together the two worlds of data warehousing and analytics with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning. The broader Azure platform includes thousands of tools, including others that interface with the various Azure databases. Many organizations that rely on Microsoft, in fact, leverage CData components for data connectivity.

Want to learn more about the most popular data warehousing platforms? Download our white paper, Cloud Data Pipeline Services, for a full exploration of each data warehouse.

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Introducing CData Sync: ETL to Support Your Data Warehousing

Organizations are increasingly moving toward simplified processes of aggregating enterprise data. A cloud-based data warehouse, coupled with third-party integrations, such as those with CRMs, can unlock the potential of enterprise data.

CData Sync enables you to instantly pipe data to any data warehouse or database destination of your choice, supporting data movement from 100+ popular data sources. With CData Sync, you can unify and backup all your data, so it will always be there, safe & sound, ready for deep, value-added data analysis.

Download a free, 30-day free trial of CData Sync to get started with your data warehousing initiatives, no matter which database or warehouse you select.