A Data warehouse is a central repository of all data of an organisation/business from various sources. DW is done by a process which involves extraction, transformation, and loading of the data. This process is commonly known as ETL (Extraction Transform Load).
- Extraction- The data is collected from multiple sources.
- Transformation- The data is converted from its previous form to a form which is required so that it can be placed into another database.
- Loading- The transformed data is loaded into the target database.
Data is extracted from one or more sources which is copied into the data warehouse. In case of large volumes of data, the data needs to be consolidated. ETL migrates the data from one database to another through a process which is required to load the data to data marts and data warehouses.
Implementing a data warehouse can be very beneficial in terms of reducing energy & time, empowering business intelligence, enrichment of data quality, consistency, and so on.
Here is the list of the top 10 prominent and efficient tools that can be of use in 2020:
BigQuery-
BigQuery by Google is a supreme data warehousing tool. This tool reduces the duration for storing and querying the datasets by means of enabling high speed SQL queries. This also controls and monitors the project and offers the feature of viewing or querying the data. This supports even geospatial analytics- location-based data can be analysed.
Amazon RedShift-
Amazon Redshift is a cost-efficient and simple tool that can analyse various types of data using standard SQL and BI (Business Intelligence) tools. It uses automatic concurrency scaling. It has an additional feature of scaling your structure or scaling between various nodes and node types. This tool uses the technology of query-optimization for running complex queries against petabytes of structured data. This results in optimised performance levels and reduces operational costs.
IBM – DataStage–
The IBM-DataStage is a business intelligence tool that integrates data across numerous enterprise systems which is built on a Hybrid data warehouse and a Logic data warehouse. It uses a high-performance parallel framework that can run on the cloud or on-premises. This tool also supports metadata management and global business connectivity and is highly secure. The best feature of this tool is that it uses graphical notations for the integration. High efficiency in low costs is given by this tool.
Microsoft SSIS-
The SQL Server Integration Service is a Data warehousing tool that is basically used for the performance of ETL (Extraction Transformation & Loading) operations. The SQL Server Integration also consists of a good set of built-in tasks. This platform has a node-based system which employees parallel processing. It`s architecture is suitable for optimizing queries. It is highly compatible with many MS Azure resources.
Teradata Corporation-
Teradata is the only commercially available Massively Parallel Processing (MPP) DW tool. This tool is one of the best for viewing large data and managing them. It can analyse large amounts of enterprise data in the cloud. It provides the process to obtain high speed access to actionable insights. The Query-Grid of Teradata can deliver the best-fit engineering. Smart-in-memory processing is employed by this tool which results in lower operational costs and good efficiency. It uses SQL to connect to commercial and open source analytics tools.
Snowflake–
The snowflake data warehousing tool can be used to set-up an enterprise level cloud data warehouse. This tool can analyse data from various unstructured and structured sources. It has a shared architecture that is separate from the processing power. The CPU resources can be scaled using this tool based on the user activities. It is highly scalable and accelerates the querying performance to deliver excellent insights in no time. The added advantage to this tool is that, it is multi-tenant which allows you to share your data across the organisation in real time (moving data is not mandatory).
PostgreSQL–
The PostgreSQL, an open source data management solution is available on cloud. Enterprises can leverage this tool as their primary resource- primary database. It also allows the detection of geospatial data. This platform can support SQL and JSON querying which can optimize the performance. Additional features such as multi-version concurrency control is also available in this data warehousing tool.
Oracle-
Oracle offers a wide range of choices including data warehousing services for both on-premises and cloud. This helps in the optimization of the customer experience by increasing the operational efficiency. This tool is highly scalable, and the data warehouse can be optimized to the fullest. It aims at providing high operational efficiency, productivity and performance with the least cost.
Vertica-
Vertica by Micro Focus is an excellent SQL data warehousing tool that is available on AWS, Azure and other such cloud-based platforms. It can also be deployed on-premise or even as a hybrid. It leverages MPP for a fast query speed and the architecture for shred resources is on point. It also has some built- in analytics capabilities such as Machine Learning, Time series & Pattern matching.
Apache- Hadoop–
The Apache Hadoop is a tool from Cloudera which can be downloaded free from the Cloudera website. It has three versions- Basic, Flex & Datahub. It is a tool that can integrate a number of datasets on to any database. Its high performance comes with the lowest cost of operation.
WIND-UP
Choosing the best Data Warehousing tool needs to be on point with respect to the scalability, affordability, deployment, customizability, performance, and maintenance. The above-mentioned guide to chose the best possible DW tool for your business can help to generate great business intelligence, business reporting and competitive advantage in the market.
