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by Haley Burton | June 22, 2021 | Last Updated: December 05, 2023

6 Best Data Warehousing Solutions for BI & Analytics

Businesses must be able to make informed decisions based on relevant data in order to be successful. But many organizations struggle to accurately operate on fragmented and disorganized data scattered across dozens or hundreds of enterprise databases and applications. A well-designed data warehouse can help organizations bridge the gap between operations and insights.

Data warehousing is a popular, powerful way to overcome data fragmentation challenges. At a high level, the process involves two steps. First, you must centralize data generated by your enterprise applications and systems into a common data warehouse. Then, you give your data analysts and decision-makers unified access to that data so they may perform analytics processes using their chosen data analytics tools.

Taken together, integrating your data into a data warehouse and running data analytics on top of that warehouse provides a simple pathway to generating insights from data. But centralizing and analyzing data requires a solid understanding of data integration, and can be a complicated and time-consuming process if you're not leveraging the right resources.

CData Software removes the complexity around data integration and replication, making it easy to access and analyze your critical business data.

The 6 types of data warehouse tools

  1. ETL tools: ETL (extract transform, load) tools are used for data integration. The software extracts data from a multitude of data sources, applications, and systems, loads it into a data warehouse, and transforms it into a suitable format for consumption.
  2. Data virtualization platforms: Data virtualization platforms create a virtual layer that acts as a bridge across multiple data sources, including warehouses, to allow users to access live data from multiple sources without having to duplicate or move it.
  3. Data modeling tools: Data modeling tools help in designing the structure of the data warehouse, including defining tables, relationships and schema. These tools help create a blueprint for data warehousing success.
  4. Data warehouse appliances: Data warehouse appliances are pre-configured hardware and software bundles that enable comprehensive data warehousing tasks. They provide benefits like rapid parallel processing, analytics, and more.
  5. OLAP tools: OLAP (online analytical processing) tools are used to analyze data by collecting and storing data from multiple sources. OLAP combines and groups data into categories to provide actionable insights for business intelligence (BI) efforts.
  6. Query and reporting tools: Query and reporting tools allow users to create and run data queries on warehoused data and generate BI reports. These tools often feature intuitive user interfaces for lines of business to easily visualize their data.

How to centralize your data in a data warehouse

Data pipeline solutions that support ETL (extract, transform, load) or ELT (extract, load, transform) enable you to pipe data from various data sources into your data warehouse.

Most companies use a variety of online and on-premises software technologies to connect different business functions. For instance, you might process orders via your ecommerce web store, manage sales in Salesforce, handle fulfillment through Amazon, and track it all through an ERP system like NetSuite. A well-designed data pipeline can extract data from all these sources, format it consistently (or better, leverage the processing power of your data warehouse), and organize it in an easily accessible manner.

Data warehousing for business intelligence improves data testing options and application development. When you can quickly access data from a broad swath of sources, you can leverage that data to create new apps.

Data consolidation with CData Sync

CData Sync is a modern solution for ETL/ELT data movement. Create and maintain a replica of your data making it easily accessible from common database tooling, software drivers, and analytics. Whether the data comes from an on-premises site or a cloud SaaS platform, CData Sync can pipeline that data to any database (traditional relational or NoSQL), data lake, or data warehouse.

CData Sync uses a simple point-and-click configuration to enable straightforward replication. Automated backups ensure that you never lose important data.

The current release of CData Sync supports automated data replication from more than 250+ enterprise data sources, and seamless integration with popular destinations like SQL Server, Snowflake, Amazon S3, Amazon Redshift, Databricks, Google BigQuery, Azure Synapse, and many more.

Learn More About CData Sync -->

How to connect analytics tools to your data warehouse

When it comes to data warehousing for analytics, synchronizing all your data is just the first step. Next, you'll need a way to get your data to your analytics tools of choice. That's why we've created CData Drivers.

CData Drivers enable you to seamlessly access data stored in your data warehouse from within every data analytics platform of consequence. Simply install CData Drivers, and you can use straightforward SQL queries to access and work with the data in your data warehouse right from your favorite analytics tool. Drivers come equipped with security features to enable data encryption, and the data models are customizable.

We even offer native connectors that embed directly into Power BI, Tableau, and Excel, so you don't even have to use SQL to access data in those tools. Simply point, click and use functions to work with your data.

CData Drivers are available for more than 250 popular tools, including every major database and analytics tool of choice. Wherever you run your data warehousing, you can easily access your data.

Why use third-party data connectors?

Data warehousing is incredibly popular, and as a result, many applications like Power BI or Tableau already support connectivity to data warehousing solutions like Snowflake or Google Big Query. So why should you consider using a third-party data connector like those from CData?

The typical reason is performance. As organizations begin to consolidate data to a data lake or data warehouse, the volume of data can often slow down analytics processing. This can be especially problematic with analytics tools that only support data ‘imports' from connected data sources – meaning that entire tables of data need to be downloaded to the analytics tool to be processed offline.

At CData, we specialize in solving these performance issues with connectivity software. Our drivers support real-time integration, passing as much query processing as possible to the underlying system and minimizing the workload of analytics processes. We understand how critical performance is to analytics and do everything possible to maximize efficiency in data warehouse integration.

Learn how CData Drivers deliver unmatched performance -->

Example use cases

Let's say, for example, you need to centralize and analyze data derived from Salesforce, NetSuite, Amazon, and Shopify.

In this case, you would first want to pipe all the information into your data warehouse and operational databases to get everything formatted consistently. With Sync, you configure simple data replication jobs that will synchronize all the data from these sources to your Snowflake data warehouse. You have the option of choosing at that point whether you will transform the data in-flight or normalizing the data using the processing power of the data warehouse.

Once you have consolidated your data, then you'll need your BI, analytics and reporting applications to connect with your data warehouse. Popular analytics and reporting tools include:

  • Looker: Utilizes real-time dashboards to provide up-to-date, detailed data analysis.
  • Looker Studio (formerly Google Data Studio): Enables centralized data analysis, perfect for analyzing data generated in the Google ecosystem and beyond.
  • Power BI: Connects to the entire Microsoft Power Platform, and includes tools like data visualizations, built-in AI capabilities, and Excel integration.
  • Tableau: Supports self-service analytics and reporting.
  • Qlik Data: Qlik Data combines a unique associative analytics engine with AI and a powerful cloud platform.

While some of these tools support some level of data warehousing integration, CData Drivers dramatically amplify performance, enabling blazing fast analytics and reporting integration. What's more, for other legacy systems used across your organization, the CData ODBC, JDBC, and ADO.NET drivers provide a consistent integration, allowing you to connect your entire analytics and reporting stack.

The 6 best data warehousing software for 2024

Here is a list of what we believe are the six best data warehousing tools to help support your data initiatives in 2024.

  1. Amazon Redshift: Amazon Redshift from Amazon Web Services (AWS) is known for its scalability and cost-effectiveness. It allows users to run complex queries on large datasets with high performance. Redshift also integrates with various AWS services, making it a good option for organizations that leverage the AWS ecosystem.

Check out CData connectivity solutions for Amazon Redshift -->

  1. Snowflake: Snowflake is a cloud-based data warehouse that offers automatic scaling, separation of storage and compute, and support for structured and unstructured data. Its modern architecture allows for easy data sharing and collaboration across organizations.

Check out CData connectivity solutions for Snowflake -->

  1. Google BigQuery: Google BigQuery, part of the Google Cloud Platform, is a serverless and highly-scalable data warehouse. Known for ease-of-use and integration with other Google Cloud services, it is great for handling large datasets and offers real-time analytics capabilities.

Check out CData connectivity solutions for Google BigQuery -->

  1. Microsoft Azure Synapse (formerly SQL Data Warehouse): Azure Synapse is part of the Microsoft Azure ecosystem. It provides a powerful platform for data warehousing and big data analytics and supports on-demand and provisioned resources, allowing users to scale based on their needs.

Check out CData connectivity solutions for Azure Synapse -->

  1. Teradata: Teradata is a long-time leader in enterprise data warehousing. It offers scalable, on-premises storage solutions suitable for large organizations with complex analytics needs.

Check out CData connectivity solutions for Teradata -->

  1. IBM Db2 Database: IBM Db2 is a cloud-native database built to power low-latency transactions, real-time analytics, and complex queries to support data needs for large enterprises.

Check out CData connectivity solutions for IBM Db2 -->

Learn more about data integration and data warehousing and get a free trial

For a good look at the cloud data pipeline market and how CData stacks up, read The Forrester Wave™: Cloud Data Pipelines, Q4 2023.

CData specializes in universal data connectivity. Whatever your preferred method of connecting data, we’re here to help. Looking for real-time connectivity with your analytics tools? Check out our CData Drivers. Ready for a data integration platform? CData Sync is your answer.

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