In the modern world today, big data and data analytics are some of the most popular on-demand technologies, people are working with. The concept of Data Warehousing and Data Analytics seemed like a new concept back in the past but today, they are some of the most important tools that are needed to cater to different services provided by major companies all around the world. Data Warehousing Tools are critical for many companies today.
Data Warehousing is the process of storing and analyzing data from multiple sources to provide meaningful business insights. It involves transforming the data from multiple sources into a common format for both storage and analysis. In general, this process is known as ETL or Extract, Load, and Transform . The most popular Data Warehouses are Amazon Redshift, Google BigQuery, and Snowflake. They all work on the Cloud.
In order to perform this process, the correct Data Warehousing Tools must be chosen because the Warehousing Tools must be simple to understand by all types of users and also to maintain all the functions of a Data Warehouse. Data Warehouses can be in-house or on the cloud. To manage these cloud-based Data Warehouses, Warehousing Tools are equally important.
These Warehousing Tools enable both the clients and employees of the data warehouse to perform the ETL process in a simplified and efficient manner. But, each tool has its own advantages and disadvantages and companies must decide which tool will best suit their purpose.
This article focuses on providing a comprehensive list of the best Data Warehousing Tools that can help different users navigate through a wide array of applications that best suit their data warehouse. It also provides a general overview of the features of a cloud Data Warehousing Tools along with the factors that need to be considered to choose the correct tool based on the line of work for your business.
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Working knowledge of SaaS applications. Working knowledge of Cloud Environments. Data Warehousing tools are used to gather, clean, transform and load data to the Data Warehouse from the data sources for storage and analysis purposes.
Data Warehousing tools are essential for running business activities and Data Analytics. It boosts performance by leveraging Artificial Intelligence and Machine Learning. Cloud Data Platform that combines structured and unstructured data into formats that are useful for Data Analytics.
Image Source As discussed in the introduction, a Data Warehousing Tool is a tool that is responsible for maintaining the data warehouse of any company. This means that they are responsible for the ETL process. So, what is ETL? ETL stands for Extract, Transform and Load and is the process of integrating data from multiple sources, transforming it into a common format, and delivering the data onto a destination usually a Data Warehouse for the analysts to perform their analysis.
Nowadays, as data warehouses are on the cloud, the Data Warehousing Tools have also adapted to be on the cloud. Before, data warehouses were built like physical warehouses to store data from heterogeneous sources and different hardware devices were involved to maintain them.
Ever since data warehouses were set up using a cloud infrastructure, the tools that were used to perform the ETL process and maintain them were also built online without any physical warehouse or hardware needed to support them.
The ETL process consists of 3 main steps:
Image Source Extract: Extraction is an important process because it integrates structured and unstructured data from multiple sources like databases, other data warehouses, files, marketing tools, CRM (Customer Relationship Management) information (etc).
Data Warehousing Tools make this process easy by allowing the user to extract valuable information with a few clicks and avoid writing complex commands or code.
Transform: Transformation is the process of converting the data extracted to a common format that can be understood by the data warehouse and the people who work to manage it. It “cleans” the data to make it in a more readable format for its users. Some transformation techniques include sorting, cleaning, removing redundant information, and verifying the data from these sources. Different tools work towards establishing this in their manner.
Load: Loading is the process of storing the transformed data onto a destination, normally a data warehouse, and also supports loading any unstructured data into data lakes that various BI (Business Intelligence) tools can use to gain valuable insights. Depending on how the Warehousing Tools work, the data can be loaded at scheduled intervals or all at once.
In order to understand, the ETL process in a more detailed fashion, click here .
Data Warehousing Tools must be used in such a way to simplify the ETL process. As most of them work on the cloud, they use the advantages of the cloud to their support. Some of the features of Data Warehousing Tools are given below.
Flexibility: The tool must be flexible to support multiple forms of data sources and integrate them in a simplified and efficient manner.Accessibility: The tool must be accessible from any device that has an Internet connection and from multiple locations. This ensures that the information entering and leaving the data warehouse is monitored.Cost-Effective: The tool must follow a pay-as-you-go pricing model and not offer high prices. As the tools are hosted on the cloud, companies need not invest in physical warehouses and hardware.Setup and Maintenance: The tool must offer easy setup and maintenance even for non-technical users. By having a cloud infrastructure, all physical devices are removed and a lot of space is preserved.Valuable Insights: The tool must be efficient and must make the data analysis-ready in real-time. It must also make the data easy for the data analyst and business analyst to gather valuable information and help make the data ready for prediction.When it comes to choosing the best the Data Warehousing Tools, many companies find it very complicated. Well, choosing the correct tool is very complicated, even for the most experienced professionals in the field.
But, depending on the application, the tool must be chosen. Here are a few factors that can be considered to make this decision easier:
Data Source: Before choosing the tool, one must analyze from what sources of data the tool extract the data for analysis. This ensures a smooth ETL process and any ingestion failures are also avoided.Scalability and Performance: Scalability is the process of understanding how the tool performs the ETL process efficiently for large amounts of data. Performance refers to the overall efficiency of the system. A tool that is more scalable offers higher performance and is more preferred.Use Cases and Maintenance: Use cases are a common feature that many companies fail to consider when choosing the tool. Companies must weigh the tools according to their service and cost along with their business goals to check if they can cater to it. Maintenance is also considered in the use case and is equally important to maintain the tool.Budget: There are multiple Data Warehousing Tools in the market and all of them are cost-effective and have different pricing models. Hence, the best tool will be the one that has a dynamic pricing model and aligns with the business goals and requirements of the company.Simplicity in Use: A tool that offers an interactive UI and is easy to learn for everyone will always be considered better than a tool that has a bad design and takes time to be used. Hence, even simplicity is an important factor when it comes to choosing the correct tool.Robustness and Application Dependability: The interaction of the tool with other applications and its ability to work with the same performance capabilities in all environments and its integration with other applications and services is also a major factor in choosing the correct tool.
Hevo Data , a No-code Data Pipeline helps to load data from any data source such as databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired data warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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Check out why Hevo is the best:
Secure : Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.Schema Management: Hevo takes away the tedious task of schema management and automatically detects the schema of incoming data and maps it to the destination schema.Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.Live Monitoring : Hevo allows you to monitor the data flow and check where your data is at a particular point in time.Sign up here for a 14-Day Free Trial!
Choosing the best tool for managing and maintaining the data warehouse and also finding one that perfectly matches the given business requirements and constraints can be a challenging task.
Hence, to simplify your search, here is a comprehensive list of the best 8 Data Warehousing Tools businesses can use to gain valuable information from their data warehouse:
Image Source Amazon Redshift is a simple, fast, and efficient Data Warehousing Tool that makes it possible to analyze data using simple SQL queries with existing Business Intelligence Tools. It can run complex analytical queries by using processes such as high-performance computing, parallel execution, uniform query optimization, and columnar storage. It was originally released in October 2012 and was based on an older version of PostgreSQL 8.0.2. The official release of the software occurred on February 15th, 2013.
Amazon Redshift uses the AWS platform and requires administrators to configure the environment by creating nodes and clusters. Hence, it is based on a clustered methodology . These clusters help in providing capacity, monitoring, and backing up the data warehouse and also to apply patches and maintain the software.
These clusters can be managed based on the user’s requirements with ease. As a beginner, users can start with either a small or single node cluster and then expand it to larger multi-node clusters as their requirements increase.
Another advantage of these clusters is that they act as checkpoints and hence their snapshot can be taken whenever concurrent transactions are being executed which maintains consistency.
Amazon Redshift protects its data at rest by default through encryption. It also offers extra protection by protecting all the data stored on the disks within a cluster and all backups in Amazon S3 with Advanced Encryption Standard AES-256 .
Amazon Redshift Use Case
Overall, Amazon Redshift is the best among Data Warehousing Tools to be used if the number of database administrators is more and people are comfortable with AWS technologies beforehand. Administrators must configure security independently for confidential transactions.
It follows the ACID property of transactions (Atomicity, Consistency, Isolation, and Durability) but there is no Data Sharing enforced. Amazon Redshift supports only 1 language- Redshift SQL. Storage issues do arise if a DC2 node is used as a cluster but no issues occur when RA3 nodes are used as a cluster. Maintenance too must be handled manually.
Pricing Model of Amazon Redshift
Image Source Amazon Redshift follows on-demand pricing and is a system based on paying at an hourly rate based on the type and node of the cluster. In case the nodes are used in a partial manner, then the bills are also filed partially and in one-second increments and following various status changes such as creation, deletion, pause, and resume the cluster. When the cluster is paused, payment is only made for the backup storage.
To learn more about the pricing of Amazon Redshift, click the link .
Image Source Google BigQuery is one of the best Data Warehousing Tools that is serverless, cost-effective, highly-scalable, and has machine learning built into it and uses the Business Intelligence Engine for its operations.
It analyzes petabytes of data using the ANSI SQL language at very fast speeds, offers insights and solutions from data across clouds with a flexible architecture, is able to store and query massive data sets in a cost-effective and efficient way.
It uses the Google Cloud Platform for operation and has a lot of columns to store data. It enables quick SQL queries to be combined with the processing power of Google’s infrastructure to manage business transactions, manage the data in different databases, and also allow access control policies for users to view and query data.
Google BigQuery also has a flexible, multi-cloud analytics solution that is powered by Anthos to analyze data across different cloud platforms. This is called the BigQuery Omni(private alpha) .
Google BigQuery also has a natural language interface for petabyte-scale analytics to operate on data sources. It helps to improve the productivity of different Business Intelligence teams and manage data from tools like Sheets and chatbots and custom applications.
Google BigQuery Use Case
Google BigQuery requires no configuration and can operate freely without the need for a database administrator. It has a fully-flexible environment and it does not require any resources to manage it.
It is one of the best Data Warehousing Tools to be used when more tables need to be managed and the number of database administrators is limited.
It also offers encrypted content by default. Google BigQuery can be used in a variety of languages including Java, .NET, or Python . Google BigQuery also supports 3rd-party applications that help to visualize data or analyze it.
Pricing Model of Google BigQuery
Image Source Google BigQuery offers a scalable and flexible pricing scheme according to every company’s budget. They offer Active or Long-term charges for their storage cost (amount of data stored on Google BigQuery) and an On-Demand or a Flat-Rate price for the query costs. Their prices are applied to Accounts and are not for Individual Projects.
To learn more about the pricing of Google Big Query, click the link .
Image Source Snowflake is an analytical Data Warehousing Tool that provides a faster, easier to use, and more flexible framework more than a normal data warehouse. Snowflake offers a complete SaaS (Software as a Service) architecture because it runs completely on the cloud.
In order to use Snowflake, no hardware or software needs to be externally or internally installed or configured. Maintenance is also handled directly by Snowflake. It can only be run using public cloud infrastructure as it uses the virtual compute instances for its computational needs and storage service for persistent storage.
All updates and patches are taken care of by Snowflake only and no other software is responsible for installing these features.
Snowflake Use Case
Companies can choose to opt for Snowflake as a Data Warehousing Tool if there is a uniform pattern of data that needs to be processed and analyzed. This is because Snowflake has an infinite storage option, is highly scalable, and supports languages such as ANSI SQL and JavaScript, and most recently it also supports Java and Python.
Similar to Amazon Redshift, it follows the ACID properties and has Data Sharing as an inbuilt feature, and can be shared from Snowflake to Snowflake without any cost or even with 3rd party vendors. Maintenance is also handled by the inbuilt software of Snowflake and data is encrypted by default during transit .
Pricing Model of Snowflake
Similar to the other Data Warehousing Tools, Snowflake has a pricing model that charges companies according to the storage they take for using Snowflake. This is done because companies can turn the computational resources on or off and hence the payment is also according to the time the resources were utilized.
They offer an “On Demand ” pricing scheme which enables access to Snowflake in a fast and easy manner with no commitments and a “Usage-Based ” pricing scheme for using a pre-purchased Snowflake package and offer billing on a per-second basis with a minimum of 60 seconds.
These prices vary according to the package, region, and platform. There are 3 types of packages- Standard , Enterprise and Business Critical . Hence, depending on the budget and resources of the company, Snowflake is a good Data Warehousing Tool to be considered.
Image Source To learn more about the pricing of Snowflake, click the link .
Image Source Microsoft Azure is a Data Warehousing Tool that is a combination of more than 200 products and cloud services that help to build, run and manage highly scalable applications across multiple cloud networks with the help of AI (Artificial Intelligence) and Machine Learning. It helps to deploy Windows and Linux virtual machines also across multiple cloud and hybrid environments.
Microsoft Azure has the Azure SQL Data Warehouse (SQL DW) , a petabyte-scalable analytical data warehouse that is built according to the SQL Server and uses the Microsoft Azure Cloud Computing Platform.
The SQL DW removes any type of physical hardware and uses data warehouse units (DWUs) that perform the computations in a highly scalable manner.
Companies that have already invested in the Microsoft Technology Stack can easily set up this tool to simplify the process of ETL. The complete Microsoft platform is accessible and any new user can use the tool up to its full power.
Microsoft Azure Use Case
Microsoft Azure can be a very good Warehousing Tool if organizations already use SQL Server and SQL Database as Azure SQL DW is built from the same technologies as them.
Similarly, companies that store data on both Azure Blob Storage and Hadoop & organizations that store data on Azure Blob Storage and HDFS will find Azure SQL DW a very easy tool because its polybase service allows users to blend the relational data stored in Azure with the non-relational data stored on Hadoop.
The Azure SQL DW is built on Microsoft Azure’s Cloud Computing platform which is easy to use and offers the full power of the Microsoft platform . It is also very flexible for users to scale the performance of their virtual machines and also add additional machines that add to the computation power of the warehouse.
Pricing Model of Microsoft Azure
Like Google BigQuery, Microsoft Azure also bills the computation and the storage separately for Azure SQL DW. The amount of DWUs is measured in terms of horsepower for computation which is the opposite of Amazon Redshift, where the hardware architecture is known to the user.
Storage is billed according to the same rate as Azure Premium Storage. Microsoft uses a pricing calculator on its website to help users estimate the total cost for both computing and storage costs. The pricing also depends on the Region , Currency, and Time . A sample of the costs for each DWU in the US East region on an hourly basis is shown below:
To learn more about the pricing of Microsoft Azure, click the link .
Image Source PostgreSQL is an open-source object-relational database that has earned a strong reputation for reliability, feature robustness, and performance. It maintains an RDBMS (Relational Database Management System) and also maintains the ACID properties similar to Amazon Redshift. It is designed to handle mid-range users such as single users, other data warehouses, and various web services.
PostgreSQL, as one of the Data Warehousing Tools, helps to make the data warehouse flexible and intelligent by allowing it to analyze, transform, model, and deliver the data within the database server.
PostgreSQL Use Case
Although it is not one of the popular Data Warehousing Tools, PostgreSQL is well known for its flexibility and its vertical and horizontal scalability. Unlike other Warehousing Tools, PostgreSQL uses the fundamental principles of databases such as primary keys, foreign keys, and database schemas and views to further enhance its simplicity. Companies can give PostgreSQL their raw data to clean it and see if they can further process it.
Pricing Mod el for PostgreSQL PostgreSQL can be combined with the other Warehousing Tools and the pricing model is adjusted according to that. The pricing is similar to the other Warehousing Tools with pricing for computation and storage. Given below is the pricing for the region of US East(Ohio) for PostgreSQL and Amazon Redshift.
To learn more about the pricing of PostgreSQL, click this link .
Image Source Teradata is an enterprise software platform that develops and sells database analytics software based on various subscriptions. It helps to unify different forms of data and deploys a hybrid multi-cloud platform. This means that deployment can be done anywhere including public clouds such as AWS, Azure, and Google Cloud, and also on-premises.
Hence, companies can choose Teradata as a good Data Warehousing Tool if there are multiple data sources that need to be analyzed and for data to be extracted.
Teradata Use Case
Teradata is best used because of its high scalability as it uses MPP (Massively Parallel Processing) to perform its computations. This means that multiple servers work in parallel to store and process data. Hence, multiple variations of big data can be processed using Teradata.
Companies that study how Access Module Processors(AMP) and Primary Index(PI) are used to allocate rows to the AMPs, can use Teradata as well.
Pricing Model for Teradata
Similar to other Warehousing Tools, Teradata also offers pricing for computation and storage. The pricing options for AWS on Teradata is shown below:
To learn more about the pricing of Teradata, click the link .
Image Source Greenplum is an open-source massive parallel platform for analytics, AI, and Machine Learning. The data analytics in Greenplum is catered to analyzing Data Transformation, Text Data, Graph Data, Time-Series Data, and Geospatial Data. It also supports programming languages such as Java , Perl , Python , pgSQL, and R .
It has an integrated database analytics platform and uses the Apache MADlib , an open-source library for in-cluster machine learning functions for PostgreSQL. It also provides multi-node, multi-GPU, and deep learning capabilities.
The query optimizer in Greenplum is the industry’s first open-source query optimizer that is designed to handle large workloads of data. It is highly scalable and uses batch mode analytics to process data and throughput.
Greenplum Use Case
Among the Data Warehousing Tools, Greenplum is a good option because of its high scalability in petabyte-scale volumes with a unique cost-based query optimizer for large scale data, its flexibility to be deployed across multiple platforms using PostgreSQL, providing a single environment for both BI and AI, and its ability to be an Open-Source platform so that the MPP Architecture, Analytical interfaces, and Security capabilities can be shared to many users.
Pricing Model for Greenplum
Users need to pay for the cost-based query optimizer in order to use Greenplum and this can be purchased by buying a Pivotal Greenplum Subscription . Pivotal Software Inc sells this subscription and the pricing is per virtual CPU.
To learn more about the pricing of Greenplum, click the link .
Image Source Netezza is a Warehousing Tool from IBM. It designs and markets high performing data warehouse appliances and advanced analytics for various data warehouses. It too is a flexible and robust platform and it has a bundled architecture including the Netezza core software and analytics within the IBM CloudPak for data system.
Netezza Use Case
Netezza is a good use case for a Warehousing Tool because it offers good flexibility and robustness when it is used. Users can use it from any location and from any data source for simplifying the ETL process.
Pricing Model for Netezza
IBM Netezza is available on IBM Cloud, Amazon Web Services (AWS), and Microsoft Azure. The pricing details are not made available and companies need to get in touch with teams of either platform in order to establish Netezza.
To learn more about the pricing of Netezza, click the link .
This article gave a comprehensive study about the best and popular Data Warehousing Tools in the market today to use in order to simplify the ETL process and manage the companies Data Warehouse. It also provided an in-depth analysis of the features, use cases, and pricing model for each of the above mentioned Warehousing Tools. Overall, every company must analyze different parameters before choosing the Warehousing Tool in order to gain valuable Business Intelligence.
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If you are looking to set up and manage a Data Warehouse in an easy and effective manner, then Hevo D ata is the right choice for you! It will simplify the ETL and Management process of both the Data Sources and the Data Warehouses.
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