Comparing Redshift and Rockset for Real-Time Analytics

$300 in free trial credits. No credit card required.

Rockset is built for real-time analytics: sub-second analytics on real-time data, without the need for additional ETL tools. Cloud data warehouses like Amazon Redshift are designed for batch analytics, which is slow and expensive for modern data apps.

10x Faster Queries

Rockset delivers sub-second query latency by indexing all your data for fast access. Redshift does not index data but relies on time-consuming scans instead. Plus they have significant query planning overhead that adds 100s of milliseconds latency to every query.

50% Lower Compute Cost

For low latency, high concurrency data apps, cost per query matters more than cost per GB as your app scales. Rockset enables compute-efficient analytics because it minimizes scans, retrieving data exclusively from indexes instead.

100% Guaranteed Fresh Data

For streaming data, ingest efficiency and low data latency matter more than storage optimization for real-time analytics. Rockset is designed to support high write rates and delivers an end-to-end data latency of two seconds.

Tackling the Challenges

Challenge #1:

Compute costs are rapidly growing

Redshift is storage optimized

Redshift organizes data into its compressed, columnar format. This is great for minimizing storage footprint and budget-friendly for analysts running occasional queries on batch data. However, querying data stored in columnar format requires computationally intensive scans, making it too expensive to run sub-second queries on fresh data.

Rockset is compute optimized

Rockset indexes all fields, including nested fields, in a Converged Index. The Converged Index is the most efficient way to organize your data. It's inspired by search and columnar indexes. This translates to a slightly bigger storage footprint in exchange for faster queries, lower data latency, and less compute costs. Rockset supports high-concurrency data applications using efficient indexing to reduce your cost per query.

Challenge #2:

Query speed is too slow

Redshift does full scans

Redshift has to scan through large portions of data to run each query, which means queries can take tens of seconds to run, especially as data size or query complexity grows. This growing complexity leads to slow performance for concurrent queries. Some try to accelerate performance by adding more costly compute, but even then, hit an upper bound for performance and cannot increase query speeds for true real-time analytics.

Rockset uses indexing to minimize scans

Rockset’s cost-based query optimizer leverages our Converged Index to automatically find the most efficient way to run low latency queries by exploiting selective query patterns within the indexed data and accelerating aggregations over large numbers of records. Rockset does not scan any faster than a cloud data warehouse. It simply tries really hard to avoid full scans altogether.

Challenge #3:

Data latency is too high

Redshift loads data in batches

Redshift loads data in batches to minimize compute processing, resulting in a delay before new data can be queried. Redshift tries to reduce this latency through delivery streams such as Kinesis to Redshift via Kinesis Data Firehose. However, though continuous, these solutions are both not real-time, as data might not be available for querying for many minutes, and incredibly expensive to run. This can be compounded by throughput constraints as the writes queue up if too much data is pushed through at one time.

Rockset makes data queryable within a second

Rockset has built-in real-time data connectors that guarantee data freshness, which no data warehouse has. Rockset’s built-in connectors for streaming event data from Amazon Kinesis and Apache Kafka ensure data is queryable within a few seconds. By using RocksDB LSM trees and a lockless protocol, Rockset enables writes to be visible to existing queries within a second of data being generated. In addition, Rockset separates compute needed for indexing from compute needed for queries to deal with bursty writes.

As you modernize your data stack to build more data applications, use Rockset to increase analytics speed and decrease costs.

Here are four reasons to use Rockset for real-time analytics:

Reduce compute costs by 50%

Increase query speeds by 10x

Reduce data latency to one second

100% serverless and built in the cloud

Rockset and AWS: Better Together

Rockset’s real-time analytics platform is easy to find, test, and purchase directly in the AWS Marketplace using AWS credits and can qualify against the Enterprise Discount Program (EDP) commitment.
Rockset ingests and indexes data from AWS for real-time analytics including Amazon DynamoDB, Kinesis, MSK, RDS for MySQL, RDS for PostgreSQL, and S3.

See Our Customers

“Being able to search, analyze, and act on [our] data in real-time is mission critical for us. We have embraced a modern serverless stack, and we chose AWS partner, Rockset,” said Doug Moore, VP of Cloud at Command Alkon.

Learn more

More from Rockset

Space-Time Tradeoff: Examining Snowflake's Compute Cost

Real-Time Data Ingestion: Snowflake, Snowpipe and Rockset

Why You Shouldn’t Build Real Time Data Apps on Data Lakes or Warehouses

Compare Rockset and Redshift

Connect with our solutions team to dig deeper into the architecture, indexing, data ingestion and query processing.

Let's Talk