For Data Engineers

FeatureBase: The Top Apache Druid Alternative

Legacy data tools were not built to power real-time analytics efficiently and data teams often find themselves looking for an Apache Druid alternative or competitor so that they can scale their workloads in a cost-effective manner. Throughout this blog post, we will share what makes FeatureBase the top Apache Druid alternative and why you should consider FeatureBase when building out your real-time data architecture.

Apache Druid for Real-Time Analytics:

Apache Druid is a real-time analytics database designed for OLAP queries on large data sets and optimized for event-oriented data. Most often, Druid powers use cases where real-time ingestion, fast query performance, and high uptime are essential. Druid is commonly used as the database backend for GUIs of analytical applications or for highly concurrent APIs that need fast aggregations. Druid works best with event-oriented data.

As organizations progress in analytical maturity and their volume of data increases, they typically see Druid hardware costs increase significantly. As a result, achieving real-time decisions may require considerably more hardware resources, especially as workloads continue to scale. Unfortunately, this limitation often leaves organizations unable to perform real-time analytics cost-effectively and is what drives them to look for an Apache Druid alternative.

Reasons to Consider Looking For An Apache Druid Alternative:

  • Your server costs to power your workloads have gotten out of control.
  • You need to perform complex JOINs on many different tables of data or very large tables (10s-100 billions of rows).
  • You regularly perform streaming updates.
  • You’re trying to deliver low-latency, high-throughput, and highly concurrent workloads with the freshest data.

There is an alternative available to help you achieve your goals without making costly tradeoffs.

FeatureBase, The Top Apache Druid Alternative:

FeatureBase is a real-time database built on bitmaps. It is designed primarily for speed and horizontal scalability, and is particularly well-suited for workloads that require many real-time updates, inserts, and deletes on massive datasets. FeatureBase ingests data continuously to execute on analytical workloads in real-time for the front lines of your business. Ingest millions of events per second with ACID compliance while simultaneously analyzing, transforming, and aggregating billions of rows of data at greater than 100x the price-performance of traditional columnar databases.

Apache Druid vs. FeatureBase:

Druid and FeatureBase have key technical differences, including data ingestion, query capabilities, and data modeling.  Let’s look at each.

Real-Time Data Ingestion:

Druid is optimized to provide analytics against massive quantities of streaming data. If you need low-latency updates of existing records using a primary key, however, you may need an Apache Druid alternative. Druid supports streaming inserts but not streaming updates. Updates must be performed via background batch jobs; updating Druid is costly and can impact performance. If you need to make frequent updates to your data, Druid may not be for you unless you can adjust your update process.

FeatureBase can handle the ingestion of massive-scale streaming data while simultaneously allowing for real-time inserts and updates to existing data schemas.

Query Capabilities:

Druid is a read-oriented analytical database, which means its write semantics are not highly fluid. While Druid supports full inner JOINs, when it comes to outer JOINs, your best bet is to leave those to the data warehouse in your stack.

FeatureBase is also optimized for reads, but it is extremely good at supporting live updates while maintaining low-latency queries. Additionally, because of our ability to collapse multiple tables into single entities and allow for multiple values within single fields, we eliminate the need for data preaggregation (JOINs), allowing organizations to operate on their freshest data while maintaining ultra low latency.

Data Modeling:

Druid is a column-oriented database that uses indexing structures to speed up query execution when a filter is provided. However, indexing structures on top of column-oriented databases increase storage overhead (and make it more challenging to allow for mutation).

FeatureBase takes a different approach to data modeling. Tables are typically modeled around entities (customers, patients, unique IDs, etc.) or events (transactions, etc.). In addition, tables can have multiple sources (batch and streaming) and update records or add new fields in real time.

Mapping relational tables to FeatureBase can be as simple as a one-to-one mapping, but major performance improvements can be made through the mapping and feature table structure depending on:

  • the expected query workload
  • the type, size, and cardinality of the data
  • the cost requirements

How to Reduce Your Number of Druid Servers:

Molecula FeatureBase is a bitmap-oriented database purpose-built for real-time analytics and machine learning. FeatureBase continuously extracts and updates features from streaming technologies like Kafka and other data sources without the need for staging or preaggregation. This superpower allows FeatureBase to serve real-time applications and analytical workloads using 50-90% fewer servers than Apache Druid.

While FeatureBase and Apache Druid provide similar real-time analytics functionality at a surface level, when you dig a bit deeper, you can see that FeatureBase’s data format built on bitmaps allows for some major gains in latency, freshness, and hardware footprint to power real-time use cases making it the top Apache Druid alternative.