Estuary

Hevo Data VS Informatica

Read this detailed 2024 comparison of Hevo Data 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 Hevo Data 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 Hevo Data 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

Hevo Data logo
Hevo Data
Informatica logo
Informatica
Estuary Flow logo
Estuary Flow
Use cases
Database replication (CDC) - sourcesHevo DataMySQL, SQL Server, Postgres, MongoDB, Oracle (ELT load only) Single target onlyInformaticaDB2, 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
Replication to ODSHevo Data

Real-time ETL in Typescript and SQL

Informatica

(Informatica Data Replication)

Estuary Flow

Requires re-extraction of sources for new destinations

Op. data integrationHevo Data

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

Informatica
Estuary Flow

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

Data migrationHevo Data

Support for SQL and Typescript

Informatica
Estuary Flow

Great schema inference and evolution support.

Support for most relational databases.

Continuous replication reliability.

Stream processingHevo Data

Pinecone (ETL) support.

Transformations can call ChatGPT & other AI APIs.

Informatica
Estuary Flow

Real-time ETL in Typescript and SQL

Operational AnalyticsHevo Data

Microbatch integrations only.

Informatica
Estuary Flow

Integration with real-time analytics tools.

Real-time transformations in Typescript and SQL.

Kafka compatibility.

AI PipelinesHevo Data

(batch ELT only)

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 connectorsHevo Data150+ connectors built by HevoInformatica300+ connectors Estuary Flow150+ high performance connectors built by Estuary
Streaming connectorsHevo DataBatch CDC, Kafka batch (source only).InformaticaYes. CDC, Kafka via PowerExchangeEstuary FlowCDC, Kafka, Kinesis, Pub/Sub
Support for 3rd party connectorsHevo Data

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

Informatica
Estuary Flow

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

Custom SDKHevo Data

(adds new 3rd party connector support fast)

Informatica

Informatica Connector Toolkit

Estuary Flow

SDK for source and destination connector development.

API (for admin)Hevo DataInformaticaEstuary Flow

API and CLI support

Core features
Batch and streamingHevo DataBatch onlyInformaticaStreaming to batch, batch to streamingEstuary FlowBatch and streaming
Delivery guaranteeHevo DataExactly once (batch only)InformaticaExactly onceEstuary FlowExactly once (streaming, batch, mixed)
Load write methodHevo DataAppend only (soft deletes)InformaticaSoft and hard deletes, append and update in placeEstuary FlowAppend only or update in place (soft or hard deletes)
DataOps supportHevo Data

No CLI, API

Informatica

(CLI, API)

Estuary Flow

API and CLI support for operations.

Declarative definitions for version control and CI/CD pipelines.

ELT transformsHevo Data

Dbt. Separate orchestration

Informatica

dbt, SQL, pushdown optimization

Estuary Flow

dbt integration

ETL transformsHevo Data

Python scripts. Drag-and-drop row-level transforms in beta.

Informatica

PowerCenter

Estuary Flow

Real-time, SQL and Typescript

Schema inference and driftHevo Data

dbt integration

Informatica

With limits

Estuary Flow

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

Store and replayHevo Data

Real-time, SQL and Typescript

Informatica
Estuary Flow

Can backfill multiple targets and times without requiring new extract.

User-supplied cheap, scalable object storage.

Time travelHevo Data

Many-to-many pub-sub ETL

Informatica
Estuary Flow

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

SnapshotsHevo Data

N/A

Informatica

N/A

Estuary Flow

Full or incremental

Ease of useHevo Data

Easy to use connectors

Informatica

Takes time to learn

Estuary Flow

streaming transforms may take learning

Deployment options
Deployment optionsHevo DataPublic cloudInformaticaOn premises, private cloud, public cloudEstuary FlowOpen source, public cloud, private cloud
The abilities
Performance (minimum latency)Hevo Data5 minutes (CDC and batch) in theory. In reality intervals are similar to others - 10s or minutes to 1 hour+ intervals.InformaticaSub-secondEstuary Flow< 100 ms (in streaming mode) Supports any batch interval as well and can mix streaming and batch in 1 pipeline.
ReliabilityHevo DataMediumInformaticaHighEstuary FlowHigh
ScalabilityHevo DataLow-Medium Row ingestion limitsInformaticaHighEstuary FlowHigh 5-10x scalability of others in production
Security
Data Source AuthenticationHevo DataOAuth / API KeysInformaticaOAuth / HTTPS / SSH / SSL / API TokensEstuary FlowOAuth 2.0 / API Tokens SSH/SSL
EncryptionHevo DataEncryption at rest, in-motionInformaticaEncryption at rest, in-motionEstuary FlowEncryption at rest, in-motion
Support
SupportHevo Data

Slow to fix issues when discovered

Informatica

Known for good support

Estuary Flow

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

Slack community.

Cost
Vendor costsHevo Data

Higher than Airbyte, 5x per GB on avg compared to Estuary

Informatica
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 costsHevo Data

Requires dbt

Limited schema evolution (reversioning)

Informatica
Estuary Flow

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

Admin costsHevo Data

Less admin and troubleshooting

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.

While Estuary Flow is also a great option for batch sources and targets, where it really shines is any combination 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 may seem low, these are connectors built for low latency and scale. In addition, Estuary is the only vendor to support Airbyte, Meltano, and Stitch connectors as well, 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 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, TypeScript (and Python coming) 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. So you can expect to pay a minimum of a few thousand per year. But it quickly becomes the lowest cost pricing. 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.

Hevo Data

Hevo Data introductory image

Hevo is a cloud-based ETL/ELT service for building data pipelines that, unlike Fivetran, started as a cloud service in 2017, which makes it more mature than Airbyte. Like Fivetran, Hevo is designed for “low code”, though it does provide a little more control to map sources to targets, or add simple transformations using Python scripts or a new drag-and-drop editor (currently in Beta) in ETL mode. Stateful transformations such as joins or aggregations, like Fivetran, should be done using ELT with SQL or dbt.

While Hevo is a good option for someone getting started with ELT, as one user put it, “Hevo has its limits”.

Pros

  • Ease of use: Like several other modern ELT tools, Hevo is intuitive and easy to use, especially compared to traditional ETL tools. 
  • ELT and ETL: Hevo has started to add ETL support including Python scripts and a new drag-and-drop editor (in Beta.) This is limited mostly to row-level transformations. Hevo’s main transformation support is dbt (ELT).
  • Reverse ETL: Hevo supports the ability to insert source data back into the source once it’s been cleansed. This might be good for you if you’re looking for this feature. It is a very specific use case where you write modified data back directly into the source. A more general-purpose solution is to have a pipeline write back to the sources, which is not supported by most modern ETL/ELT vendors. It is supported by iPaaS vendors.

Cons

  • Connectivity: Hevo has the lowest number of connectors at slightly over 150. While this is a lot, it means you need to think about what sources and destinations you need for your current and future projects to make sure it will support your needs. 
  • Latency: Hevo is still mostly batch-based connectors on a streaming Kafka backbone. While data is converted into “events” that are streamed, and streams can be processed if scripts are written for any basic row-level transforms, Hevo connectors to sources, even when CDC is used, is batch. There are starting to be a few exceptions. For example, you can use the streaming API in BigQuery, not just the Google Cloud Storage staging area. But you still have a 5 minute or more delay at the source. Also, there is currently no common  scheduler. Each source and target frequency is different. So latency can be longer than the source or target when they operate at different intervals.  
  • Costs: Hevo can be comparable to Estuary for low data volumes in the low GBs per month. But it becomes more expensive than Estuary and Airbyte as you reach 10s of GBs a month. Costs will also be much more as you lower latency because several Hevo connectors do not fully support incremental extraction. As you reduce your extract interval you capture more events multiple times, which can make costs soar.
  • Reliability: CDC is batch mode only, with the minimum interval being 5 minutes. This can load the source and even cause failures. Customers have complained about Hevo bugs that make it into production and cause downtime.
  • Scalability: Hevo has several limitations around scale. Some are adjustable. For example, you can get the 50MB Excel, and 5GB CSV/TSV file limits increased by contacting support. 
    But most limitations are not adjustable, like column limits. MongoDB can hit limits more often than others. A standalone MongoDB instance without replicas is not supported. You need 72 hours or more of OpsLog retention. And there is a 4090 columns limit that is more easily hit with MongoDB documents. 
    There are ingestion limits that cause issues, like a 25 million row limit per table on initial ingestion. In addition there are scheduling limits that customers hit, like not being able to have more than 24 custom times.
    For API calls, you cannot make more than 100 API calls per minute.
  • DataOps: Like Airbyte, Hevo is not a great option for those trying to automate data pipelines. There is no CLI or “as code” automation support with Hevo. You can map to a destination table manually, which can help. But while there is some built-in schema evolution that happens when you turn on auto mapping, you cannot fully automate schema evolution or control the rules. There is no schema testing or evolution control. New tables can be passed through, but many column changes can lead to data not getting loaded in destinations and moved to a failed events table that must be fixed within 30 days or the data is permanently lost. Hevo used to support a concept of internal workflows, but it has been discontinued for new users. You cannot modify folder names for the same “events”. 

Pricing

Hevo is more expensive than Airbyte and Estuary, but still less expensive than Fivetran and various ETL vendors.

  • Free: Limited to 1 million free events per month with free initial load, 50+ connectors, and unlimited models
  • Starter ($239/mo for 5M rows): Offers 150+ connectors, on-demand events, and 12 hours of support as an SLA. Additional rows are $10 or more per million (~1GB)
  • Business (Custom Pricing): HIPAA compliance with a dedicated data architect and dedicated account manager

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 easy to automate. 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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