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Confluent
Self-serve streaming data platform for building real-time ETL from DB, SaaS and filestores. Company behind Gazette and Estuary Flow OSS.
Self serve tool for creating a Kafka cluster and using it for real-time ETL from DB's and Filestores. Company behind Apache Kafka.
n/a
Open-Source, or predictably priced pipelines at $0.50 / GB plus $0.14 / hr (~$100/mo) for any capture or materialization.
Open Core and predictably priced based on several metrics. Initially you'll need a kafka cluster and then will be billed on connectors that you use, data transferred, and data stored.
Estuary's pricing saves 70% or more depending on your data scale.
<100ms. Only constraint is frequency of updates from the source, or what the destination can handle.
<100ms. Only constraint is frequency of updates from the source, or what the destination can handle.
N/A
100+ connectors. Also HTTP file, webhook, and ability to spin up most new connectors within a week.
60+ connectors with half built in-house and half open-source. Materialization connectors use at-least once semantics vs exactly once and no capability for data reduction.
Extracting data from more of your systems can enhance outcomes. Exact semantics helps ensure data accuracy.
Estuary Flow supports private deployments and BYOC (Bring Your Own Cloud) to handle all private networking environments.
Yes
Estuary Flow’s Private Deployments require minimal setup from the customer side while self-hosting Confluent requires operational work.
Exactly-Once
At-least Once
At-least once semantics can create duplicates the destination, creating inaccurate results and excess cost.
Automated Schema Evolution.
Users manage the Schema Registry to validate and evolve data and schema.
Automation ensures that your destination always matches your source.
Ingested data stored in a real-time data lake in customers cloud storage.
Data is stored in the Kafka Kafka topic at ($0.10/gb/mo). This leads to significant costs and results in many users pushing data to a batch system.
By storing data in a real-time data lake, you can endlessly distribute in real-time off one ingest, saving you egress fees, money, and source system stress.
Streaming SQL and javascript transforms with joins on both real-time and historical data. DBT as a destination.
Single-Message Transforms can perform basic transforms of a single message or can use ksql for broader streaming joins. Soon to be Flink with the Immerock acquisition.
Estuary Flow unlocks unlimited lookback joins.
Pinecone
None
Teams quickly demanding support for vector DBs
No
Joins are done in ksql and require windowing
Unlimited windowing lookback enables use cases like customer 360.
We're creating a new kind of DataOps platform thatempowers data teams to build real-time,data-intensive pipelines and applications, at scale,with minimal friction, in a UI or CLI. We aim to make real-time data accessible to the analyst, while bringing power tooling to the streaming enthusiast. Flow unifies a team's databases, pub/sub systems, and SaaS around their data, without requiring new investments in infrastructure or development.
Estuary develops in the open to produce both the runtime for our managed service and an ecosystem ofopen-source connectors. You can read more about our story here.