Top 4 Data Warehouse Tools for Collecting and Managing Business Data

The data analytics software market is constantly growing, and users nowadays have difficulty deciding what options are worth the money and the time. This is no different regarding data warehouse tools used to collect and manage data.

The purpose of a data warehouse is to process, transform, and ingest data, thus supporting decision-making within an organization. This way, a data warehouse acts as a singular central repository of integrated data from multiple sources.

However, you need a good tool for data collection and management since this is the only way to make the whole set of processes go smoothly. Take a look at various warehousing tools that will help you collect and manage business data easily.

BigQuery

BigQuery is a famous data warehouse owned by Google. This option is a fully managed data warehouse which is serverless and enables scalable analysis of large chunks of data. It is a PaaS which supports querying and is equipped with integrated capabilities for machine learning.

Thanks to the fact that this tool is developed by Google, BigQuery has external access to Google’s Dremel technology. This interactive ad hoc query system is scalable and intended for the analysis of data which is nested. The tool requires authentication of all requests and also supports various Google-proprietary mechanisms along with OAuth.

Its main functionalities include:

• Data management – Creating and deleting objects such as views, tables, and user-defined functions.
• Access control – The ability to share datasets with groups, individuals, or the world.
• Query – Expresses queries in an SQL dialect, and provides results in JSON.
• Integration – You can use this tool from Google Apps Script or other languages which work with client libraries or its REST API.
• Machine learning – Creating and executing models of machine learning through SQL queries.

This tool is really one of the best options out there. However, if pricing plans don’t match your budget or you would like a slightly different tool, keep in mind that there are good BigQuery alternatives that you can use to get the same results. You can further look into all of their functionalities and opt for the one that would work best for you.

Amazon Redshift

Amazon Redshift is included in the Amazon Web Services cloud-computing platform. This tool is based on technology used for massive parallel processing to manage the migration of databases and large-scale data sets.

As it can handle analytic workloads on big-data data sets which are stored by the DBMS principle which is column-oriented, this tool is different from Amazon’s other hosted database offerings (Amazon RDS). It allows as much as 16 petabytes of data on a cluster, which is significantly more than Amazon RDS’s size of 16TB.

This tool relies on compression and parallel processing, decreasing the time needed for command execution, which allows it to manage billions of rows at the same time. Therefore, it is very good for analyzing and storing big amounts of data from live feeds or logs using Amazon Kinesis Data Firehose or another similar source.

Snowflake

Snowflake has picked up a lot of media attention recently due to an astounding IPO. It definitely is another great warehouse option, and some would go so far as to say that it is the main rival of BigQuery.

Snowflake was built for smooth analysis, and that’s exactly what it provides. Users that rely on Snowflake enjoy effective resource allocation as well as an SQL workbench which can handle various data types smoothly, all the while benefiting from data governance and strong security protocols.

Keep in mind, however, that Snowflake doesn’t come with data integrations. This means that users will have to use an ETL tool that will move their data into the warehouse. So, bear in mind that using a third-party pipeline most certainly brings additional costs.

PostgreSQL

PostgreSQL has been around for over three decades. It is a well-known object-relational database left to be open-source. It is deemed to be a top-level SQL server because of its set of functionalities, great performance, as well as dependability.

Many large-scale corporations across a wide range of business niches (from gaming to eCommerce) use this database solution. It is especially popular with devs, and it has a community where it is backed by a great number of developers and coding enthusiasts.

In its essence, it is a database system, so its users have to rely on ETL software to push the data wherever necessary.

Its admin tools are great, but some users report that they can be a bit challenging at times. Experienced data engineers will have no problems with PostgreSQL’s configuration options, but other users might have a hard time handling and dealing with its complex setup.

Final thoughts

Don’t be surprised if you don’t see some of the more popular options here since the data analytics space is very crowded at the moment. However, these names deserve to be on this list, and they are there for a good reason.

As a customer, you should know that you have many options, and you will not have a difficult time finding a solution that has the right functions for you at the right price.