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Metabase is a business intelligence tool that allows organizations to question their data. The answers are then displayed in useful and shareable formats.
Metabase has various use cases. For example, it creates dashboards rich KPIs and other critical metrics used in an organization. The platform also gives vital updates to these metrics.
Metabase also allows users to share performance indicators, objectives, and results.
It also performs real-time analytics. Users can share the results of these analytics with their team members. This allows team members to keep track of key performance indicators. It also makes explorative analysis easier.
Metabase is a BI tool. It is also a powerful visualization tool.
It makes data more accessible and actionable. This way, users can easily uncover and share insights with their teams.
Metabase users can create dashboards, schedule email reports, and use a graphical interface to ask simple questions.
The Metabase interface is based on SQL.
Metabase also integrates with other databases and data warehouses. These include Mongodb, BigQuery, Spark, Amazon, Snowflake, Redshift, and Postgresql.
Metabase helps you analyze and visualize your data so you can use it to make business decisions. However, to use data effectively, you must first integrate then put it into structures that make it easy to ask questions. This process involves ETL and ELT.
ETL stands for:
E (extract) -- getting data from an application or any other service you use
T (transform) -- transforming data through cleaning, filtering, aggregating, combining and enriching. The clean data is organized to make it easier to create models.
L (load) -- loading involves storing data in a data warehouse.
Basically, ETL is the process of transferring unrefined data from a root source to the target storage.
The major difference between ETL and ELT is that in ETL the data transformation happens outside the data warehouse.
A typical ETL pipeline looks like this: Data sources > Distributed processing software > Data warehouse.
A typical ELT pipeline looks like this: Data sources > Extraction tool> Data warehouse > Transformation jobs
Some time back, ETL was more common than ELT. These days, people simply load raw, untransformed data into a data warehouse. Once the data is in a warehouse, it is transformed and organized into forms that are easier to analyze.
Reverse ETL is the process of copying data from a data warehouse into an application. This application can be a market automation, CRM software such as Salesforce and Hubspot, or analytics software among others.
Reverse ETL sits on the opposite side of ETL on the data pipeline. While ETL fulfills data integration, reverse ETL fulfills data activation.
After data is transformed, it is loaded into a data warehouse. The data can be loaded all at once or in small batches depending on the business equipment being used. The loading process also depends on the data source, and the ETL tools used. The amount of time it takes to load the data varies depending on the system being used.
Metabase comes in 3 pricing tiers including a starter, pro, and enterprise suite. You can also choose to use the open-source version that is available free of charge.
No matter where your business is in its journey, there is a Metabase pricing plan that suits its needs. All plans come with a 14-day free trial.
This plan is a user-friendly BI suite. It is perfectly suited for startups and small businesses. The suite is deployed in the cloud and goes for $ 85/month and $5 per user per month.
The starter pack offers:
3-day email support
Migration from open source
Connect to 20+ database types
Out-of-the-box SMTP server
Use any 15+ visualization types
Automated Upgrades and backups
Schedule Updates via email or slack
Fully managed cloud
The Metabase pro suite is an upgrade of the starter suite. This plan is perfect for businesses and teams that are growing. The pro suite is deployed in the cloud. IT goes for $500 a month for 10 users and then $10 per user per month for additional users.
The Metabase pro suite offers everything in the starter pack plus:
3-day email support
Single sign-on via SAML, JWT, or advanced LDAP
White labeling: customize logos, colors, and more
User, dashboard, and table-level data access audit logos
This suite is suitable for large organizations and teas. The plan comes with everything in the starter and pro plans plus:
The enterprise suite is deployed in the cloud. Prices vary depending on the specific organization or team. You have to speak to the sales department to get a quote.
Metabase is a valuable tool for data-driven businesses. To help you better understand the value of Metabase, let's compare it with Tableau. Tableau is a data visualization tool that works similarly to Metabase.
Metabase comes with an excellent UX interface. It is easy to create charts and tables even for users of who have no analytics experience.
Metabase also offers valuable guidance and suggestions on the visualizations being developed.
Additionally, users can feed data directly into the database. This makes it easy to create visualizations by dragging and dropping.
Conversely, Tableau is better for performing more complex data analytics.
Metabase is a powerful data intelligence solution with a solid dashboard and diverse functionality. The platform uses big data as its source of truth and offers a great dashboard.
Metabase is well known for:
Drag and drop interface
Connectivity to numerous data sources
Scheduled email reports
User-friendly SQL editors
online analytic processes
customized and interactive dashboards
Metabase integrates with several databases and data warehouses. Including Mongo, Spark, Snowflake, MySQL, Postgres, Redshift, Amazon, and Big Query.
Similarly, Tableau is linked to Cari's file maker, Diffbot, Incentive Solution, Cortex, email meter, Koros marketing, and Fluix.
Although Metabase has a ton of benefits, it is not a perfect tool. Here are some downsides of Metabase.
Metabase lacks such features that would enhance its functionality. Examples include ad hoc reports, NLG, predictive analytics, trend indicators, benchmarking, and performance metrics. The platform also comes with very few widgets.
Reviews of the Metabase platform show that it tends to crash graphs at random points. It also sometimes fails to load or crashes when users are loading data. This makes it difficult to stay on project timelines when transferring data.
Data governance in Metabase is severely limited. Most users will have to use CTEs with SQL to work around the data governance issue, leading to a new set of code issues. Worse still, the platform offers little support for code versioning and user authentication.
The enterprise version of Metabase is too expensive. This pricing beats logic considering that the platform lacks important features.
However, the starter and pro versions are affordable.
The world is moving towards the ELT approach. However, there are scenarios where it's better to use the ETL method. Such scenarios include:
when you have complicated data transformations also known as transforms
when you want to run machine learning on the data before you load the data into a data warehouses
when you need to change the format of the data to fit specific specifications of the database.
ETL tools help businesses retrieve historical data. This data provides context and in-depth comprehension of the company
ETL tools bring out meaningful patterns and insights. They also convert assorted data in a consistent format
ETL tools enhance and provide seamless business intelligence solutions for decision making
Some of the best ETL tools that every business should consider include:
Portable is the best data integration tool for businesses with long-tail data sources.
Portable is both an ETL and ELT platform. It comes with 300+hard-to-find data sources.
The tool also comes with custom connectors and free ongoing maintenance of long-tail connectors.
Portable is best for teams whose focus is on gleaning data insights rather than develop data pipelines.
Infomatica is a collection of high performance data visualization tools. The tool comes with governance, analytics and integration features.
The tool is best for teams seeking a robust comprehensive solution for their data needs.
IBM database is popular among organizations with legacy infrastructures. This is generally because of its faster efficiency.
IBM features parallel processing capabilities, and quality features.
This tool is best for teams that already use other IBM tools.
ZigiOps is a tool for streamlining data workflows.
The tool comes with real-time integration and no-code features.
ZigiOps is best for teams that want numerous data integration services.
SAP is a suite of tool such as SAP Cloud Platform and SAP Data Integrator.
The tool offers efficient batch processing for big data workflows. It also has a platform for cleansing, quality and integration.
SAP is best for teams that are familiar with the SAP suit of data products.