Speed — How much hardware do you need to run your analysis?
Maintenance — Automation that improves your database
Data amount — Defines how much space you need
Data type support — Things like integers, characters, strings, floating-point numbers, and arrays
Cost — Does it fit your budget?
There are hundreds of databases to choose from. Let's look at the features, pros, and cons of the five databases we recommend for embedded dashboards:
Snowflake's Data Cloud is a modern data warehouse. Snowflake's data storage, processing, and analytic solutions are far quicker, more straightforward, and more versatile than conventional services.
What Does Snowflake Do?
Snowflake blends cutting-edge architecture created explicitly for the cloud with a brand-new SQL query engine. It offers users all of the features and capabilities of an enterprise analytic database along with a host of other features, like:
Excellent control over data layout and indexing
Advanced data-sharing features
Separate storage and computing payments
Robust semi-structured/JSON data support
Automated maintenance and scalability
Tons of new developer features
Why We Like Snowflake
Snowflake is a great choice if you're looking for a database with reporting as an analytics data cloud rather than a transactional database. It's also good if you're looking to share and govern data across organizational boundaries.
Check out these Snowflake reviews to see how it might fit your needs. Here's a summary for you:
Solves traditional hardware-based data warehouse concerns,including:
Data transformation problems
Delays or failures brought on by high query rates
Scales up virtual warehouses to take advantage of additional computing resources
Helpful if you need to load data more quickly or execute a large number of queries
Facilitates data sharing and governance across Snowflake tenants
Explo's Perspective on Snowflake for Embedded Dashboards
At Explo, we recently worked with a company in the food-tech space to revamp and improve analytics for the restaurants they serve.
They wanted to showcase application data, but their existing data model and application database weren't designed to surface valuable insights for their customers. Snowflake allowed them to pull all their data into a centralized warehouse and create materialized views for analytics-specific tables.
Because of this optimization, they reduced dashboard load times to just a few seconds while scaling to hundreds of customers. Rather than spending months writing code to fit the unique needs of customer-facing analytics, organizations can deploy production-ready data sharing capabilities in a matter of days
Rockset is a real-time analytics warehouse that offers operations-light searches on vast amounts of semi-structured data.
It automatesconfiguring, deploying,anddenormalizing clusters— along with shard and index management.
Rockset can ingest data and begin running queries in around 15 minutes, depending on the amount of data collected.
What Does Rockset Do?
Rockset creates a schema for your data automatically, which enables SQL queries for data sources without native SQL capabilities.
Rockset's other capabilities include:
All columns are indexed by default
No need for an additional ETL tool, and no lag, so it’s real-time
Fast query speeds are great for complex queries. Better for smaller data scale but more complex, real-time queries
Easy management with simple cluster sizes to choose from
A Flexible data model supported
Why We Like Rockset
Rockset can take in large data streams
Indexes the data so it's queried within two seconds
Allows a high number of concurrent SQL queries
Even for complicated queries, Rockset provides quicker results than traditional databases due to Converged Index.
Explo's Perspective on Rockset for Embedded Dashboards
A retail consulting company chose to implement Rockset to speed up their customer reporting. Prior to Rockset, they were streaming data through Kafka and used MongoDB as their primary database.
Rockset allowed them to centralize all their data into a high-performant database without an additional ETL tool. In addition, Rockset automatically reads in and supports semi-structured data and indexes all the fields, so once the data was loaded in, it was ready to query.
All this took less than a day to set up and was ready to plug into an embedded analytics solution to share insights with their retailers.
ClickHouse is an open-source database that has a cloud-hosted version available. Performance-wise, it beats every other column-oriented database management system.
What Does ClickHouse Do?
Each ClickHouse server is capable of:
Processing tens of gigabytes of data in billions of rows per second
Using column store optimized for clickstream analytics
Completing analysis jobs that traditional databases can't, such as:
Running fast query speeds
Traditional databases are often too expensive, or their data volume is too big to evaluate quickly with queries
Supporting multiple concurrent queries
This is great for customer-facing analytics
Responding with low latency
ClickHouse performs on substandardhardware better than traditional databases.
Why We Like Clickhouse
ClickHouse is great if you're looking for aggregation over a specific column in large volumes of data.
Multiple engine options for adapting user cases
Easy configuration of data replication
Explo's Perspective on Clickhouse for Embedded Dashboards
A cloud communications platform leveraged ClickHouse for its embedded analytics. Speed and scalability were their highest priorities as they wanted to showcase near real-time data for thousands of clients.
With Clickhouse's new cloud offering, they no longer need to spin up and manage their own database, which saved their developers days to ramp up and even more on ongoing maintenance.
Their new dashboard provides crucial metrics on API usage with load times at 3x faster than their previous solution.
Postgres is an open source object-relational database system that supports:
Developers — in creating applications
Administrators — in safeguarding data integrity and creating fault-tolerant systems
You — in managing your data regardless of dataset size
What Does Postgres Do?
Postgres lets you create new functions, specify your data types, and even write code in several programming languages without recompiling your database.
Postgres complies with SQL and supportsmost of the SQL standard's key capabilities.
Postgres also has:
A robust access-control system
Column and row-level security
Multiple cloud-hosted vendor options
Why We Like Postgres
Postgres is easy to set up, popular, and compatible with about any tool
Startups and smaller companies can create a read-replica of their Postgres database without worrying about additional data infrastructure
Explo's Perspective on Postgres for Embedded Dashboards
A retail platform startup leverages its existing Postgres database to surface sales and inventory analytics to its customers. By spinning up a read replica of their current application database, they spun up analytics for their customers in minutes.
As a startup, they can also iterate quickly on their data structure and run simple queries fast to show their retailers as they build their platform.
BigQuery is a big data warehouse. It offers built-in technologies — machine learning, geospatial analysis, and business intelligence — to collect and analyze your data.
BigQuery's scalable, distributed analytical engine allows you to query terabytes of data in seconds and petabytes in minutes.
BigQuery uses a columnar structure for data storage that is ideal for analytical queries. It supports database transaction semantics and displays data in tables, rows, and columns. BigQuery storage is automatically mirrored across several locations to maximize availability.
Why We Like BigQuery
BigQueryworks well with Google Suite
highly scalable with large amounts of data
Fully managed, so it's easy to set up and doesn’t need much tuning
Explo's Perspective on BigQuery for Embedded Dashboards
A retail analytics company created dashboards for their direct-to-consumer clients using BigQuery.They showcase challenging-to-calculate but mission-critical metrics clearly and concisely.
They designed a simple UI that is fully managed and scalable. Initially, they didn’t know all their data sources and what their schema would be. So, they wanted a solution that allowed them to rewrite tables and update schemas easily.
With Explo, they connected directly to their existing databases and warehouses without replicating data or creating new data models. They copied a few lines of code and utilized Explo's API to embed our interactive dashboards and reports.
Embedded dashboards are a vital component of customer experience.
Before implementing an embedded dashboard, choose a database that can power an embedded analytics application.
Evaluate your infrastructure, current technologies, and resources.
These options can help you get started.But, before you create your implementation plan, consider the additional demand on your tool and downstream databases, the cost, and the type and amount of data you have.