Rockset vs. BigQuery for Real-Time Analytics

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BigQuery was designed for batch analytics, which is too slow and expensive for modern data apps. Rockset, in contrast, is built for real-time analytics: sub-second analytics on real-time data, without the need for additional ETL tools.
10x Faster Queries
Rockset delivers sub-second query latency by indexing all your data for fast access. BigQuery 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
BigQuery requires you to cobble together disparate systems to ingest data in real-time. Rockset has native, out of the box connectors to your data sources that ensure real-time data.

Tackling the Challenges

Challenge #1:

Compute costs are rapidly growing

BigQuery is not compute optimized

BigQuery 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, which combines an inverted index, a columnar index and a row index. This translates to a slightly bigger storage footprint in exchange for faster queries, lower data latency, and less compute costs.

Challenge #2:

Query speed is too slow

BigQuery does full scans

BigQuery 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. And each query requires a minimum of 100s of milliseconds of start-up time. 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

BigQuery data is stale

BigQuery loads data in batches to minimize compute processing, resulting in a delay before new data can be queried. Some data warehouses try to reduce this latency by continuously loading small data batches, such as Snowpipe on Snowflake. 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 guarantees data freshness, which no data warehouse has. 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

See Why Companies Choose Rockset for Real-Time Analytics

Modern companies are building real-time logistics tracking, security analytics, predictive maintenance and more in record time.

See Our Customers

Ritual uses Snowflake for ad-hoc analysis, periodic reporting and machine learning model creation, but knew that Snowflake would not meet their sub-second latency requirements for personalization and looked to Rockset as a potential speed layer.

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