Estuary

Debezium + Kafka VS Informatica

Read this detailed 2024 comparison of Debezium + Kafka vs Informatica. Understand their key differences, core features, and pricing to choose the right platform for your data integration needs.

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Comparison between Debezium + Kafka and Informatica
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Introduction

Do you need to load a cloud data warehouse? Synchronize data in real-time across apps or databases? Support real-time analytics? Use generative AI?

This guide is designed to help you compare Debezium + Kafka vs Informatica across nearly 40 criteria for these use cases and more, and choose the best option for you based on your current and future needs.

Comparison Matrix: Debezium + Kafka vs Informatica vs Estuary Flow

Debezium + Kafka logo
Debezium + Kafka
Informatica logo
Informatica
Estuary Flow logo
Estuary Flow
Use cases
Database replication (CDC)Debezium + KafkaCommon databases supported Real-time replication (sub-second to seconds)InformaticaDB2, MySQL, SQL Server, Oracle, Postgres, IBM i and Z/OS sources (PowerExchange)Estuary FlowMySQL, SQL Server, Postgres, AlloyDB, MariaDB, MongoDB, Firestore, Salesforce, ETL and ELT, realtime and batch
Operational integrationDebezium + Kafka

No integration features

Informatica
Estuary Flow

Real-time ETL data flows ready for operational use cases.

Data migrationDebezium + Kafka

Well suited for ongoing replication.

Handles schema changes.

Informatica
Estuary Flow

Great schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Stream processingDebezium + Kafka

kSQL, SMTs

Informatica
Estuary Flow

Real-time ETL in Typescript and SQL

Operational analyticsDebezium + Kafka
Informatica
Estuary Flow

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

AI pipelinesDebezium + Kafka

Kafka support by vector database vendors, custom coding (API calls to LLMS, etc.)

Informatica

Pinecone and Databricks Vector Database

Estuary Flow

Pinecone support for real-time data vectorization.

Transformations can call ChatGPT & other AI APIs.

Connectors
Number of connectorsDebezium + Kafka100+ Kafka sources and destinations (via Confluent, vendors)Informatica300+ connectors Estuary Flow150+ high performance connectors built by Estuary
Streaming connectorsDebezium + KafkaMost common OLTP databases supported for CDC Community-maintained connectorsInformaticaCDC, Kafka via PowerExchangeEstuary FlowCDC, Kafka, Kinesis, Pub/Sub
3rd party connectorsDebezium + Kafka

Kafka ecosystem

Informatica
Estuary Flow

Support for 500+ Airbyte, Stitch, and Meltano connectors.

Custom SDKDebezium + Kafka

Kafka Connect

Informatica

Informatica Connector Toolkit

Estuary Flow

SDK for source and destination connector development.

Core features
Batch and streamingDebezium + KafkaStreaming-centric (subscribers and pick up in intervals)InformaticaStreaming to batch, batch to streamingEstuary FlowBatch and streaming
Delivery guaranteeDebezium + KafkaAt least once for most destinationsInformaticaExactly onceEstuary FlowExactly once (streaming, batch, mixed)
ELT transformsDebezium + Kafka
Informatica

dbt, SQL, pushdown optimization

Estuary Flow

dbt integration

ETL transformsDebezium + Kafka

Minimal via SMTs

Informatica

PowerCenter

Estuary Flow

Real-time, SQL and Typescript

Load write methodDebezium + KafkaYes (identical data by topic)InformaticaSoft and hard deletes, append and update in placeEstuary FlowAppend only or update in place (soft or hard deletes)
DataOps supportDebezium + Kafka

CLI, API

Informatica

CLI, API

Estuary Flow

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

Schema inference and driftDebezium + Kafka

Support for message-level schema evolution (Kafka Schema Registry) with limits by source and destination

Informatica

With limits

Estuary Flow

Real-time schema inference support for all connectors based on source data structures, not just sampling.

Store and replayDebezium + Kafka

Requires re-extract for each destination

Informatica
Estuary Flow

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Time travelDebezium + Kafka
Informatica
Estuary Flow

Can restrict the data materialization process to a specific date range.

SnapshotsDebezium + Kafka

Supports incremental and full snapshots

Informatica

N/A

Estuary Flow

Full or incremental

Ease of useDebezium + Kafka

Takes time to learn, set up, implement (OSS)

Informatica

Takes time to learn

Estuary Flow

Low- and no-code pipelines, with the option of detailed streaming transforms.

Deployment options
Deployment optionsDebezium + KafkaOpen source, Confluent Cloud (Public)InformaticaOn premises, private cloud, public cloudEstuary FlowOpen source, public cloud, private cloud
Abilities
Performance (minimum latency)Debezium + Kafka< 100 msInformaticaSub-secondEstuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.
ReliabilityDebezium + KafkaHigh (Kafka); Medium (Debezium)InformaticaHighEstuary FlowHigh
ScalabilityDebezium + KafkaHigh (GB/sec)InformaticaHighEstuary FlowHigh 5-10x scalability of others in production
Security
Data source authenticationDebezium + KafkaSSL/SSHInformaticaOAuth / HTTPS / SSH / SSL / API TokensEstuary FlowOAuth 2.0 / API Tokens SSH/SSL
EncryptionDebezium + KafkaEncryption in-motion (Kafka for topic security)InformaticaEncryption at rest, in-motionEstuary FlowEncryption at rest, in-motion
Support
SupportDebezium + Kafka

Low (Debezium community)

Informatica

Known for good support

Estuary Flow

Fast support, engagement, time to resolution, including fixes.

Slack community.

Cost
Vendor costsDebezium + Kafka

Low for OSS

Informatica

Opaque pricing based on "Informatica Pricing Units"

Estuary Flow

2-5x lower than the others, becomes even lower with higher data volumes. Also lowers cost of destinations by doing in place writes efficiently and supporting scheduling.

Data engineering costsDebezium + Kafka

OSS infrastructure

Informatica

Complex product with a steep learning curve

Estuary Flow

Focus on DevEx, up-to-date docs, and easy-to-use platform.

Admin costsDebezium + Kafka

OSS infrastructure

Informatica
Estuary Flow

“It just works”

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Estuary Flow

Estuary introductory image

Estuary was founded in 2019. But the core technology, the Gazette open source project, has been evolving for a decade within the Ad Tech space, which is where many other real-time data technologies have started.

Estuary Flow is the only real-time and ETL data pipeline vendor in this comparison. There are some other ETL and real-time vendors in the honorable mention section, but those are not as viable a replacement for Fivetran. Estuary Flow is also a great option for batch sources and targets.

Where Estuary Flow really shines is in any combination of change data capture (CDC), real-time and batch ETL or ELT, and loading multiple destinations with the same pipeline. Estuary Flow currently is the only vendor to offer a private cloud deployment, which is the combination of a dedicated data plane deployed in a private customer account that is managed as SaaS by a shared control plane. It combines the security and dedicated compute of on-prem with the simplicity of SaaS.

CDC works by reading record changes written to the write-ahead log (WAL) that records each record change exactly once as part of each database transaction. It is the easiest, lowest latency, and lowest-load for extracting all changes, including deletes, which otherwise are not captured by default from sources. Unfortunately ELT vendors like Airbyte, Fivetran, Meltano, and Hevo all rely on batch mode for CDC. This puts a load on a CDC source by requiring the write-ahead log to hold onto older data. This is not the intended use of CDC and can put a source in distress, or lead to failures.

Estuary Flow has a unique architecture where it streams and stores streaming or batch data as collections of data, which are transactionally guaranteed to deliver exactly once from each source to the target. With CDC it means any (record) change is immediately captured once for multiple targets or later use. Estuary Flow uses collections for transactional guarantees and for later backfilling, restreaming, transforms, or other compute. The result is the lowest load and latency for any source, and the ability to reuse the same data for multiple real-time or batch targets across analytics, apps, and AI, or for other workloads such as stream processing, or monitoring and alerting.

Estuary Flow also has broad packaged and custom connectivity, making it one of the top ETL tools. It has 150+ native connectors that are built for low latency and/or scale. While this number may seem low, these are high-quality, standardized connectors. In addition, Estuary is the only vendor to support Airbyte, Meltano, and Stitch connectors, which easily adds 500+ more connectors. Getting official support for the connector is a quick “request-and-test” with Estuary to make sure it supports the use case in production. Most of these connectors are not as scalable as Estuary-native, Fivetran, or some ETL connectors, so it’s important to confirm they will work for you. Flow’s support for TypeScript and SQL transformations also enables ETL.

Pros

  • Modern data pipeline: Estuary Flow has the best support for schema drift, evolution, and automation, as well as modern DataOps.
  • Modern transforms: Flow is also both low-code and code-friendly with support for SQL and TypeScript (with Python on the way) for ETL, and dbt for ELT.
  • Lowest latency: Several ETL vendors support low latency. But of these Estuary can achieve the lowest, with sub-100ms latency. ELT vendors generally are batch only. 
  • High scale: Unlike most ELT vendors, leading ETL vendors do scale. Estuary is proven to scale with one production pipeline moving 7GB+/sec at sub-second latency.
  • Most efficient: Estuary alone has the fastest and most efficient CDC connectors. It is also the only vendor to enable exactly-and-only-once capture, which puts the least load on a system, especially when you’re supporting multiple destinations including a data warehouse, high performance analytics database, and AI engine or vector database.
  • Deployment options: Of the ETL and ELT vendors, Estuary is currently the only vendor to offer open source, private cloud, and public multi-tenant SaaS.
  • Reliability: Estuary’s exactly-once transactional delivery and durable stream storage makes it very reliable.
  • Ease of use: Estuary is one of the easiest to use tools. Most customers are able to get their first pipelines running in hours and generally improve productivity 4x over time. 
  • Lowest cost: For data at any volume, Estuary is the clear low-cost winner in this evaluation. Rivery is second.
  • Great support: Customers consistently cite great support as one of the reasons for adopting Estuary.

Cons

  • On premises connectors: Estuary has 150+ native connectors and supports 500+ Airbyte, Meltano, and Stitch open source connectors. But if you need on-premises app or data warehouse connectivity, make sure you have all the connectivity you need.
  • Graphical ETL: Estuary has been more focused on SQL and dbt than graphical transformations. While it does infer data types and convert between sources and targets, there is currently no graphical transformation UI.

Pricing

Of the various ELT and ETL vendors, Estuary is the lowest total cost option. Estuary only charges $0.50 per GB of data moved from each source or to each target, and $100 per connector per month. Rivery, the next lowest cost option, is the only other vendor that publishes pricing of 1 RPU per 100MB, which is $7.50 to $12.50 per GB depending on the plan you choose. Estuary becomes the lowest cost option by the time you reach the 10s of GB/month. By the time you reach 1TB a month Estuary is 10x lower cost than the rest.

Debezium + Kafka

Debezium started within Red Hat following the release of Kafka, and Kafka Connect. It was inspired in part by Martin Kleppmann’s presentations on CDC and turning the database inside out.

Debezium is the open-source option for general-purpose replication, and it does many things right for replication, from scaling to incremental snapshots (make sure you use DDD-3 and not whole snapshotting). If you are committed to open source, have the specialized resources needed, and need to build your own pipeline infrastructure for scalability or other reasons, Debezium is a great choice.

Otherwise, think twice about using Debezium because it will be a big investment in specialized data engineering and admin resources. While the core CDC connectors are solid, you will need to build the rest of your data pipeline including:

  • The many non-CDC source connectors you will eventually need. You can leverage all the Kafka Connect-based connectors to over 100 different sources and destinations. But they have a long list of limits (see confluent docs on limits).
  • Data schema management and evolution - while the Kafka Schema Registry does support message-level schema evolution, the number of limitations on destinations and the translation from sources to message makes this much harder to manage.
  • Kafka does not save your data indefinitely. There is no replay/backfilling service that manages previous snapshots and allows you to reuse them, or do time travel. You will need to build those services.
  • Backfilling and CDC happens on the same topic. So if you need to redo a snapshot, all destinations will get it. If you want to change this behavior you need to have separate source connectors and topics for each destination, which adds costs and source loads.
  • You will need to maintain your Kafka cluster(s), which is no small task.

If you are already invested in Kafka as your backbone, it does make good sense to evaluate Debezium.

Pros

  • Real-Time CDC: Kafka + Debezium captures database changes in real-time.
  • Flexibility: Debezium supports multiple databases and allows for flexible configuration options for filtering and handling database changes.
  • Scalable Data Streams: Kafka’s distributed architecture ensures that even high-velocity data streams are processed efficiently and can scale horizontally.

Cons

  • Complex Setup: Managing a self-hosted Kafka cluster alongside Debezium requires significant operational effort, including scaling, monitoring, and ensuring fault tolerance.
  • At-Least-Once Delivery: Debezium guarantees at-least-once delivery, meaning duplicate records may need to be handled at the consumer level. This adds complexity to building exactly-once data pipelines.
  • High Infrastructure Costs: Running a Kafka cluster and Debezium connectors, especially at scale, can require substantial infrastructure resources, making it more costly than other CDC alternatives.

Pricing

Kafka itself is open-source and free to use, but the costs associated with deploying and maintaining a Kafka cluster can vary depending on cloud or on-premise infrastructure. Managed Kafka services such as Confluent Cloud can provide a more streamlined, albeit pricier, solution. Debezium is open-source, but operational costs come from the Kafka infrastructure and any associated storage, processing, and egress costs.

Informatica

Informatica introductory image

Informatica started as one of the first ETL vendors with Powercenter, in 1993. It then released one of the first cloud integration products, Informatica Cloud, in 2006. Informatica Cloud was originally built based on an older version of Informatica PowerCenter. After PowerCenter started to get replaced by a new Hadoop (Spark)-based framework, Informatica Cloud eventually moved over as well. After being taken private by Permira in 2015 and having a long period as a private company, Informatica became publicly traded again in 2021.

Informatica is perhaps the best example of a mature data integration platform. While it was one of the first to make the transition to the cloud and has one of the strongest and broadest data integration feature sets, it is harder to use, more expensive, and not as DataOps-native. But it has great enterprise features and one of the better private cloud architectures.

Pros

  • A comprehensive data management platform: Informatica Intelligent Data Management Cloud is much more than ETL-based data integration. It includes dozens of options including replication, data quality and master data management.
  • Rich data integration functionality: Informatica has developed a rich library of capabilities over the years for data integration. 
  • Great connectors: Over 300 connectors, including proven connectors to high-performance on premises and cloud data warehouses.
  • Performance and scalability: Informatica is built to support large deployments and deliver low latency data pipelines at scale. Informatica has supported serverless compute, pipeline partitioning, push-down optimization, and other features for years.
  • Private cloud: Informatica is one of the few vendors that supports a private data plane deployment managed by a shared SaaS control plane.

Cons

  • Harder to learn: While Informatica Cloud is easier than Powercenter was, it still has a significant learning curve compared to most SaaS ELT services. This makes it more suitable for larger, specialized data integration teams.
  • Doesn’t support DataOps as well: Informatica Cloud was built pre-CI/CD and DataOps. While you can use its CLI and API to automate deployment, it’s not as simple as a more modern platform. Schema evolution is supported, but there are some limitations depending on the source and destination. Versioning and other tasks are harder than some of the more modern ELT tools.
  • Higher vendor costs: Informatica is more expensive than most other ELT and ETL vendors.

Pricing

Informatica’s consumption-based pricing is complicated and requires a quote. You can read the Informatica Cloud and Product Description Schedule here. Cloud is mostly based on hourly pricing per compute units, with some other pricing like row-based pricing for CDC-based replication. In general, you can expect a higher cost compared to most other vendors.

How to choose the best option

For the most part, if you are interested in a cloud option, and the connectivity options exist, you may choose to evaluate Estuary.

Modern data pipeline: Estuary has the broadest support for schema evolution and modern DataOps.

Lowest latency: If low latency matters, Estuary will be the best option, especially at scale.

Highest data engineering productivity: Estuary is among the easiest to use, on par with the best ELT vendors. But it also has delivered up to 5x greater productivity than the alternatives.

Connectivity: If you're more concerned about cloud services, Estuary or another modern ELT vendor may be your best option. If you need more on-premises connectivity, you might consider more traditional ETL vendors.

Lowest cost: Estuary is the clear low-cost winner for medium and larger deployments.

Streaming support: Estuary has a modern approach to CDC that is built for reliability and scale, and great Kafka support as well. It's real-time CDC is arguably the best of all the options here. Some ETL vendors like Informatica and Talend also have real-time CDC. ELT-only vendors only support batch CDC.

Ultimately the best approach for evaluating your options is to identify your future and current needs for connectivity, key data integration features, and performance, scalability, reliability, and security needs, and use this information to a good short-term and long-term solution for you.

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