dbt

dbt

dbt Labs
5.0 out of 5 stars
Informatica Data Engineering dbt

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Informatica Data Engineering:

About

Ingest, prepare, and process data pipelines at scale for AI and analytics in the cloud. Informatica’s comprehensive data engineering portfolio provides everything you need to process and prepare big data engineering workloads to fuel AI and analytics: robust data integration, data quality, streaming, masking, and data preparation capabilities. Rapidly build intelligent data pipelines with CLAIRE®-powered automation, including automatic change data capture (CDC) Ingest thousands of databases and millions of files, and streaming events. Accelerate time-to-value ROI with self-service access to trusted, high-quality data. Get unbiased, real-world insights on Informatica data engineering solutions from peers you trust. Reference architectures for sustainable data engineering solutions. AI-powered data engineering in the cloud delivers the trusted, high quality data your analysts and data scientists need to transform business.
dbt:

About

dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
Informatica Data Engineering:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported
dbt:

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported
Informatica Data Engineering:

Audience

Data analysts and business experts searching for an operational data fabric solution
dbt:

Audience

SQL users looking for a ETL solution to engineer data transformations
Informatica Data Engineering:

Support

Phone Support Supported
24/7 Live Support Not Supported
Online Supported
dbt:

Support

Phone Support Not Supported
24/7 Live Support Supported
Online Supported
Informatica Data Engineering:

API

Offers API Not Supported
dbt:

API

Offers API Not Supported
Informatica Data Engineering:

Screenshots and Videos

dbt:

Screenshots and Videos

Informatica Data Engineering:

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported
dbt:

Pricing

$100 per user/ month
Free Version Supported
Free Trial Supported
Informatica Data Engineering:

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software
dbt:

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 4.8 / 5
design 4.8 / 5
support 4.2 / 5
dbt:

Pros & Cons from Real Users

Pros

  • Ease of use and Features. Easy to setup, integrate, and get started quickly Less maintenance Out of the box CI/CD integration with Git Easy to learn.
  • - Credential and version management is offloaded to the cloud - Simple-to-use orchestration - Seamless state management - Integrated documentation and lineage - Collaborative development experience - Native CI/CD integration - Centralized logging and observability - Enterprise-grade access control and auditability - Easy environment management - Rapid onboarding for new users
  • We use dbt for our data transformations. It's been a game changer from a Data Engineering and Analytics Engineering standpoint. It has accelerated our migration from legacy systems and made our pipelines 80% faster. We have increased visibility in our projects, a catalog and many other data quality indicators.
  • dbt has been one of the most transformative tools in my data career. It gives teams a clean, maintainable way to translate business logic into reliable, production-grade data models. It standardizes the entire development lifecycle — modeling, testing, documentation, version control, CI/CD, and lineage — in a way that allows analytics engineers and data engineers to work with clarity and confidence. It’s the backbone of our governed analytics strategy. Exceptional developer workflow: Modular SQL, version control, built-in testing, documentation, and macros allow us to scale complex business logic with consistency and reliability. Scales with organizational change: dbt has allowed us to redesign core product and customer analytics with patterns that are resilient to future product launches and schema changes.

Cons

  • Limited product Usage metrics. Product usage insights/Metrics can be better. Metrics around AI usage by developers with in the product will help.
  • - Individual capabilities are not as robust as dedicated tools. for example, orchestration is simple to use but lacks the flexibility, customization, and advanced scheduling logic of dedicated orchestrators
  • I think that the pricing model can easily become a barrier. The cost per model run is a terrible bottleneck for us and affects our capacity to architect following best practices.
  • dbt IDE could be more flexible with Git operations. Advanced users would benefit from features like git stash, more granular branch management, and better conflict-resolution tools directly in the IDE. This would remove friction during rapid iteration or when working across multiple branches. More built-in patterns for complex incremental modeling would be helpful for teams dealing with very high data volumes and dynamic product schemas.
Informatica Data Engineering:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Supported
dbt:

Training

Documentation Supported
Webinars Supported
Live Online Supported
In Person Supported
Informatica Data Engineering:

Company Information

Informatica
Founded: 1993
United States
www.informatica.com/products/big-data.html
dbt:

Company Information

dbt Labs
Founded: 2016
United States
www.getdbt.com
Informatica Data Engineering:

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Informatica Data Engineering:

Categories

Big Data Supported
Data Engineering Supported
Data Pipeline Supported
dbt:

Categories

Big Data Supported
Data Catalog Supported
Data Engineering Supported
Data Integration Supported
Data Lineage Supported
Data Modeling Supported
Data Pipeline Supported
Data Preparation Supported
Data Quality Supported
DataOps Supported
ETL Supported
Semantic Layer Supported
dbt:

Big Data Features

Collaboration Supported
Data Blends Not Supported
Data Cleansing Supported
Data Mining Not Supported
Data Visualization Not Supported
Data Warehousing Not Supported
High Volume Processing Not Supported
No-Code Sandbox Not Supported
Predictive Analytics Not Supported
Templates Not Supported

Data Lineage Features

Database Change Impact Analysis Supported
Filter Lineage Links Supported
Implicit Connection Discovery Not Supported
Lineage Object Filtering Not Supported
Object Lineage Tracing Not Supported
Point-in-Time Visibility Not Supported
User/Client/Target Connection Visibility Not Supported
Visual & Text Lineage View Not Supported

Data Preparation Features

Collaboration Tools Supported
Data Access Not Supported
Data Blending Supported
Data Cleansing Supported
Data Governance Not Supported
Data Mashup Not Supported
Data Modeling Not Supported
Data Transformation Not Supported
Machine Learning Not Supported
Visual User Interface Not Supported

ETL Features

Data Analysis Not Supported
Data Filtering Supported
Data Quality Control Supported
Job Scheduling Not Supported
Match & Merge Not Supported
Metadata Management Not Supported
Non-Relational Transformations Not Supported
Version Control Not Supported
Informatica Data Engineering:

Integrations

Acryl Data Not Supported
Azure Marketplace Not Supported
Cake AI Not Supported
Cargo Not Supported
DataOps.live Not Supported
Datafold Not Supported
Decube Not Supported
FairCom EDGE Supported
GetDot.ai Not Supported
Hex Not Supported
Kestra Not Supported
Lightdash Not Supported
LocalStack Not Supported
Metaplane Not Supported
MetricSign Not Supported
Quaeris Not Supported
Select Star Not Supported
Union Cloud Not Supported
Validio Not Supported
Zipher Not Supported
dbt:

Integrations

Acryl Data Supported
Azure Marketplace Supported
Cake AI Supported
Cargo Supported
DataOps.live Supported
Datafold Supported
Decube Supported
FairCom EDGE Not Supported
GetDot.ai Supported
Hex Supported
Kestra Supported
Lightdash Supported
LocalStack Supported
Metaplane Supported
MetricSign Supported
Quaeris Supported
Select Star Supported
Union Cloud Supported
Validio Supported
Zipher Supported
Informatica Data Engineering:
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Claim Informatica Data Engineering and update features and information
dbt:
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