The competition within the data analytics space is heating up, with all the players aim to make data collection and analysis more manageable. One of the leading technologies making waves is BigQuery.
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Ease of scaling and seamless integration with Google products makes BigQuery a favorite for many users. However, BigQuery's technical learning curve may put it outside of the reach of analytics teams that don’t have a dedicated data engineer.
The good news is that there are other accessible tools similar to BigQuery but identifying the right one is challenging in a crowded marketplace.
To help, we compared some of the different characteristics like costs, ease of use, and features. Here 5 data warehousing tools that you can use instead of BigQuery:
Panoply is a data warehouse and ETL platform with pre-built tools that make it easier to seamlessly access, sync, manage, and store data. It's easy to set up, maintain, and access as it's designed as an out-of-the-box full-stack data solution.
Although Panoply was developed for data analysts, you don't have to be one to use it. Anyone with a good understanding of SQL can get a data pipeline up and running within a matter of minutes. This frees up your time to focus on analysis, whether you’re running queries directly in Panoply or in your favorite BI tool.
Another plus with Panoply is its world-class support. While the platform is designed to be easy to use, you have the confidence that if you do run into issues, there'll be someone on the other end of the line to help.
Panoply pricing: see all pricing options; a free trial is available.
2. Amazon Redshift
As the most proven tool in this category, Amazon Redshift is a fully managed cloud-based data warehouse used to collect and store data. Like BigQuery, Redshift seamlessly integrates with multiple products and ETL services.
With Redshift, data is stored in the cloud for seamless analysis. But Redshift lacks data connectors to bring data in from disparate sources, creating a need for third-party tools.
While Redshift helps users analyze data from different sources using BI tools, implementation can take weeks or months and small teams may find that they don't have the necessary time, skills, or wherewithal to maintain a cluster. Complex pricing considerations and the need (or at least, desire) for pricey additional features can also be a blocker for Redshift.
Amazon Redshift pricing: is based on data consumption and billed on a per-second basis (they also offer a two-month free trial).
Snowflake, which recently made headlines for a record IPO, is another top cloud data warehousing platform. It goes head-to-head with BigQuery in that both are built store data for seamless analysis.
When companies use Snowflake's cloud warehousing technology, they benefit from the rapid allocation of resources, a SQL workbench that easily handles many data types, robust security protocols, and data governance.
Snowflake has different options when it comes to costs, but like Redshift, they all follow a consumption-based pricing model. While it's certainly an advantage, it can also work against you because you won't know what you'll pay until you use it.
Plus, Snowflake doesn’t include data integrations, so teams will have to bolt on an ETL tool to pipe their data into the warehouse. Those third-party pipelines add extra cost and overhead in the form of setup and maintenance that some teams may not want to absorb.
Snowflake pricing: based on a per-second data consumption model (with an option of a 30-day free trial).
For over three decades, the open-source object-relational database system PostgreSQL has maintained its reputation as a top SQL server due to its features, performance, and reliability. (Heck, Redshift is even based on Postgres!) It's the go-to database solution for large corporations and organizations across a variety of industries from ecommerce to gaming to telecommunications.
PostgreSQL is a popular choice among app developers and is actively supported by a vibrant community. As it's essentially a database system, you'll have to use an ETL tool like Microsoft SSIS, IBM InfoSphere DataStage, Oracle GoldenGate, or Talend Open Studio to push your data into storage.
Postgres’s administration tools are powerful (and worth learning), but they're known to be challenging. While sophisticated data engineers are likely to appreciate Postgres’s flexible configuration options, other users may chafe at the complexity of its setup and maintenance.
Postgres pricing: open-source and free (but you have to manage everything yourself).
5. Microsoft Azure SQL Data Warehouse
Azure SQL Data Warehouse, now subsumed by Azure Synapse Analytics, brings together the worlds of big data analytics and enterprise data warehousing. Over the years, Azure has made a name for enabling the seamless transfer of data between on-premise and cloud ecosystems.
Microsoft's shift to a unified platform creates a closed ecosystem for ingesting, preparing, managing, and serving data for immediate BI and ML needs. Its serverless on-demand or provisioned resources enable users to query data quickly and at scale.
As Azure Synapse Analytics is cloud-based, you can design the data structure right away without any implementation headaches. But there's a learning curve when it comes to understanding the Azure environment (and it’s not going to be a hit with those who aren’t fans of Microsoft).
Like Snowflake, Azure Synapse follows a consumption-based pricing model. So, you have to be sure about how much and what you're going to use it for before committing.
Azure pricing: follows an hourly data consumption model (and offers a 12-month free trial).
Comparing ETL Tools
The data analytics space is now crowded and highly competitive. This is great for customers as they have more options when it comes to features and costs. But as always, the right platform for you depends on your project's scope, your ability (or willingness) to manage ETL pipelines, and your budget.