Best Data Warehousing Tools for Small & Mid Enterprises in 2021

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Best Data Warehousing Tools for Small & Mid Enterprises in 2021

Data warehouses that were once known as data centres have now moved to cloud due to all good reasons. With a large volume of businesses moving to the cloud, data warehousing technology is in full swing, and the experts call it the third wave of innovation. If you are new to this topic, you must be thinking of what data warehouse is.

Well, data warehousing is a system that collects data across disparate sources such as CRM, ERP, etc. and stores it in one centralized data repository. The data analyst then uses this comprehensive data repository for reporting and analytic purposes. Several reasons are responsible for this substantial technological transition.

  • Cloud’s pay-as-you-go model
  • Faster, real-time data processing
  • Reduced software maintenance fees
  • Zero to minimal hardware setup cost
  • Works with big data formats and structures
  • Elasticity and flexibility to scale big datasets

With the advent of data warehousing technology businesses track a drastic improvement in access to critical information and speedy query response times. Data analysts can gain better insights into big data to fetch useful information.

Availability of cloud data warehousing tools enhances the functional benefits of the technology to greater extents.

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Let’s elaborate some of the best data warehousing tools that small and medium businesses can employ on a pay-per-use basis.

Microsoft Azure 

Microsoft Azure is a cloud-based relational database with unlimited data and analytics capabilities. Azure provides users with the flexibility to choose from wide-ranging services, including compute, storage, networking, and analytics for developing new applications at scale.

Businesses can manage their internal data-related challenges for meeting end goals. Not just Microsoft Azure is compatible with open-source applications and technologies, but it also offers tools and features meant for all industry verticals.

With a node-based system, it employs MPP (Massively Parallel Processing) and is compatible with thousands of Azure resources. It is an appropriate option for high-concurrency environments, hence allows for speedy retrieval and visualization of business insights.

As an Azure user, you don’t have to manage the server infrastructure as it allows you to design, deploy and manage applications at scale. It charges users on a pay-as-you-go basis, so you only have to pay for the services and computing resources you used.

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Snowflake 

A Snowflake is an enterprise-grade tool, available as DWaaS (Data Warehouse as a Service), build from the Cloud up and works with structured and semi-structured data sources. It allows users to work with multiple workloads using the traditional, familiar, standard SQL.

Snowflake is built on a unique, multi-clustered, shared architecture specially designed for the cloud. Though it incorporates features similar to other cloud data warehouses, it is powered with additional functionalities and best-in-class capabilities.

Since it separates storage from the processing power; it allows to scale computing resources based on business demands and user’s activity. As the scalability improves, the platform also enhances query performance to provide access to better, faster actionable insights.

By having Snowflake, the business teams can share data across organizations as and when required. While most of the highly used data warehousing tools bill users based on the total amount of data processes, Snowflake pricing structure is based on per-second billing.

Amazon Redshift

Amazon Redshift is a fully-managed, high-performance, petabyte-scale cloud data warehousing solutions designed for massive dataset storage and analysis. Redshift is Amazon’s data warehousing tool that consists of several clusters of databases comprising dense storage nodes.

Highly used in organizations that require large volume database migrations as the analytics database is Designed to handle large amounts of data efficiently. With the capability to scale to a petabyte, it allows users to begin with few gigabytes of data.

Amazon Redshift is an ideal option for enterprises who are looking for faster query processing on large datasets as speedy processing and Management is impossible to attain with traditional data warehousing technology.

Organizations who wish to use their business-critical data for acquiring new business insights can leverage Amazon Redshift. Cost-wise, Amazon Redshift is a go-to option for many businesses as the services are highly affordable and charge businesses only for what they use.

Teradata

Hybrid, multi-cloud architecture of Teradata makes it portable and flexible to deploy over anywhere, including AWS, Google Cloud, Azure, or on-premise. When it comes to database services and solutions, Teradata is considered as a leading market player.

Teradata is RDBMS (relational database management system) that works over the concept of parallel processing to allow users to monitor and analyze data efficiently. Terabyte offers two different categories, including data analytics and marketing applications.

Since Teradata is based on MPP (Massively Parallel Processing), it divides the processes and workloads across the entire systems and runs it in parallel to accomplish the tasks quickly and efficiently.

It deploys multiple analytic engines for providing access to the right tools for completing a job faster. Similar to all primary data warehousing tools, Teradata’s pricing is also based on a pay-as-you-go model.

Google BigQuery

BigQuery is a serverless, multi-cloud data warehouse tool from the house of Google that comes with powerful functionality and improved query performance. The cost-effective data warehouse solution incorporates Machine Learning capabilities, meant to deliver business agility.

It separates storage and compute; thus, it allows for scaling memory resources and processing based on business-specific requirements. It is possible to integrate it with TensorFlow and Cloud ML for creating robust, powerful, modern Artificial Intelligence -led models.

BigQuery ML feature allows data analysts and data scientists to build highly-optimized Machine Learning models on structured or semi-structured data, using simple SQL. BigQuery BI Engine allows analyzing large and massive datasets faster with quick query response times and high concurrency.

BigQuery provides separate pricing models for queries and storage. Querying has two different pricing models that include on-demand and flat-rate. On the other hand, pricing for storage is based on an active or long-term usage basis.

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Conclusion

With the availability of highly scalable, cost-efficient data warehouses services, enterprises of all size can unlock the potential of business data. The data warehousing technology is here to stay forever as it is considered a game-changer in the way the businesses used to think and operate.