12 Best Data Engineering Tools & Platform in 2025

As data volumes and complexity grow, engineering teams need tools that simplify integration, transformation, and observability.

The problem is, scaling pipelines isn’t just about adding servers or speeding up queries. 

When observability and governance are missing, growing data pipelines can degrade silently—causing performance bottlenecks, broken dependencies, and unreliable insights at critical moments.

In this guide, we’ll explore why data engineering is a cornerstone for businesses today, the challenges that hold teams back, and how to streamline, monitor, and scale their pipelines.

By the end, you’ll know how to build smarter, faster data pipelines that keep your business running smoothly, even as data demands keep growing.

TL;DR – 12 Best Data Engineering Tools & Platforms (2025)

1. Upsolve – Best for observability, governance, and data lineage - fast deployment and enterprise-grade compliance.
   
   

2. Apache Spark – Ideal for large-scale distributed data processing with high scalability.
   
   

3. Power BI – Great for business intelligence and easy-to-use analytics dashboards.
   
   

4. Sisense – Strong embedded analytics and customization for SaaS teams.
   
   

5. Airflow –  An open-source platform for orchestrating and automating complex data pipelines.
   
   

6. Snowflake – Cloud-native warehouse with robust scalability and compliance.
   
   

7. Google BigQuery – Serverless, cost-efficient analytics for Google Cloud users.
   
   

8. AWS Glue – Automates ETL workflows with seamless AWS integration.
   
   

9. Looker – Best for data exploration and visualization within Google Cloud.
   
   

10. Databricks – Unified platform for analytics, ML, and AI workloads.
    
    

11. Talend – Reliable for data integration and ETL across hybrid environments.
    
    

12. ThoughtSpot – AI-driven analytics with intuitive search and smart insights.

What are Data Engineering Tools?

Data engineering tools help teams collect, process, and manage data efficiently. 

They’re designed to build data pipelines that move information from different sources, clean it, and prepare it for analysis or reporting.

Must Have Features of a Data Engineering Tool

A modern data engineering tool should simplify data operations, automate workflows, and scale with your business,  here are the key features it must include:

• Pipeline building – Collect, clean, and move data seamlessly from multiple sources to target systems.
  
  
  

• Workflow automation – Reduce manual effort with automated scheduling and task orchestration.
  
  
  

• Real-time monitoring – Track pipeline health, detect bottlenecks, and fix errors before they escalate.
  
  
  

• Anomaly detection – Spot unusual patterns or data quality issues early to maintain reliability.
  
  
  

• Governance & compliance – Ensure sensitive data follows regulations with audit logs and security controls.
  
  
  

• Integration ecosystem – Connect easily with cloud platforms, databases, and BI tools for smooth workflows.
  
  
  

• Scalability – Handle growing data volumes without sacrificing performance or efficiency.

How We Evaluated These Data Engineering Tools? 

Here’s how we’ve evaluated all the 12 data engineering platforms:

• Each tool was evaluated based on how it addresses real-world data engineering challenges across industries.

• Customer reviews and expert insights were analyzed to identify strengths and limitations in practical use.

• Adoption speed was considered by looking at how easily teams can implement the tool without major setup or training.

• Support quality was judged through the availability of documentation, tutorials, and active user communities.

•  Tools prioritizing r cloud-native systems, AI-driven analytics, and modern data governance.

12 Best Data Engineering Tools & Platforms

1. Upsolve: Embedded Analytics for SaaS Teams

Alt Text: Upsolve Home Page

Upsolve provides a no-code analytics layer that lets SaaS teams embed dashboards, KPIs, and reports directly inside their applications.

It connects to data warehouses like Snowflake and BigQuery, automates query optimization with AI, and updates visualizations in real time; all without manual engineering. 

Key Features for Data Engineering

• Real-Time Dashboards: Instantly display live data updates within your application.
  
  
  

• Embedded Business Intelligence: Integrate analytics directly into your product using iFrame or React components.
  
  
  

• Multi-Tenant Support: Manage and display data for multiple clients or users securely.
  
  
  

• Customizable Styling: Tailor the appearance of dashboards to match your brand's identity.
  
  
  

• AI-Driven Analytics: Leverage artificial intelligence to provide insights and predictive analytics.
  
  
  

• Scheduled Reporting: Automate the delivery of reports via email at specified intervals.
  
  
  

• CSV & PDF Exports: Allow users to download data in various formats for offline analysis.
  
  
  

• End-User Personal Workspaces: Provide individual analytics environments for each user.
  
  
  

• Data Plane Connections: Connect to multiple data sources for comprehensive analysis.
  
  
  

• Single Sign-On (SSO): Enhance security and user convenience with SSO integration.

Pricing 

• Growth Plan: From $1,000+/month with dashboards, 50 tenants, and core analytics.

• Professional Plan: From $2,000+/month with unlimited dashboards, AI analytics, and support.

• Enterprise Plan: Custom pricing with full access, compliance, and 24/7 support

Pros

• Handles high-volume data reliably without errors.
  
  
  

• Offers clear observability into all data flows.
  
  
  

• Scales effectively as data loads increase.
  
  
  

Cons

• Performance can drop if underlying systems face downtime.
  
  
  

• Misconfigured data governance can propagate errors.
  
  
  

• Unconventional workflows may require workarounds.
  
  
  

Best For

SaaS companies seek to embed advanced analytics and AI business intelligence into their products without extensive development efforts.

2. Apache Spark: Real-Time Analytics for Big Data Workloads  

Alt Text: Apache Spark Home Page

Apache Spark is a data engineering tool, best for large-scale distributed data processing. It offers in-memory analytics for high-performance computing and integrates seamlessly into existing pipelines, making it a go-to for enterprise-scale workloads.

Key Features for Data Engineering

• Distributed Computing: Processes massive datasets across clusters efficiently.
  
  
  

• In-Memory Processing: Speeds up tasks by storing data in memory rather than disk.
  
  
  

• Fault Tolerance: Ensures reliability by automatically handling failures during execution.
  
  
  

• Advanced Analytics: Supports machine learning, graph processing, and streaming.
  
  
  

• Multi-Language Support: Enables development in Java, Scala, Python, and R.
  
  
  

• Batch & Stream Processing: Handles both static and real-time data workflows.
  
  
  

• Data Integration: Connects with Hadoop, Kafka, and various data lakes.
  
  
  

• Resource Management: Optimizes computing resources dynamically during execution.
  
  
  

• Scalability: Easily scales from a single server to thousands of nodes.
  
  
  

• Extensive Ecosystem: Supports integrations with popular tools and libraries for seamless workflows.

Pricing 

Spark Streaming has not publicly disclosed pricing for its services. Apache Spark offers a usage-based pricing model, where costs are incurred per second for Data Compute Units (DCUs), accelerators, and storage usage.

Pros

• Apache Spark processes both batch and real-time streaming data using a single engine across multiple languages.

• It enables training machine learning algorithms on a laptop and scaling the same code to clusters for petabyte-scale analysis.

• Spark SQL adapts execution plans at runtime, automatically optimizing joins and reducers for faster query performance.
  
  
  

Cons

• Running Spark on small datasets consumes excessive resources.
  
  
  

• Optimal performance requires complex cluster and job tuning.
  
  
  

• Micro-batch streaming can introduce processing latency.
  
  
  

• Debugging distributed jobs is difficult due to verbose logs.

Best For

Enterprises and data teams process large-scale, complex datasets that require speed, scalability, and advanced analytics across distributed systems.

3. Power BI - Microsoft’s Analytics Platform for Business Intelligence  

Alt Text: PowerBI Home Page

Power BI operates at the analytics layer, complementing data engineering workflows by transforming processed data into interactive dashboards, AI-driven insights, and reports. 

Integrated with Microsoft 365 and Azure, it enables business users to access and interpret data quickly without building pipelines or managing infrastructure.

Key Features for Data Engineering

• Interactive Dashboards: Build and share visual, real-time reports.
  
  
  

• Natural Language Queries: Ask questions in plain English with the Q&A feature.
  
  
  

• AIInsights: Identify patterns, trends, and anomalies.
  
  
  

• Data Source Integrations: Connect to hundreds of cloud and on-premise sources.
  
  
  

• Collaboration Tools: Share dashboards securely across Teams, Excel, and SharePoint.
  
  
  

• Row-Level Security: Protect sensitive data with granular permissions.
  
  
  

• Scheduled Data Refresh: Keep dashboards up to date automatically.
  
  
  

• On-Premises & Cloud Options: Deploy via Power BI Report Server or Microsoft Fabric.
  
  
  

Pricing

• Free Account: $0/month
  
  
  

• Power BI Pro: $14/user/month (billed annually)
  
  
  

• Power BI Premium Per User: $24/user/month (billed annually)
  
  
  

• Power BI in Microsoft Fabric: Variable pricing (capacity-based)
  
  
  

Pros

• Power BI allows seamless embedding of dashboards in Microsoft 365 apps like Teams and Outlook.
  
  
  

• AI-powered features like Copilot and natural language queries simplify report creation.
  
  
  

• Real-time data updates enable teams to make decisions based on the latest information.
  
  
  

Cons

• Power BI Desktop is Windows-only, lacking native support for macOS and Linux.
  
  
  

• The platform struggles with large datasets, often leading to performance issues and timeouts.
  
  
  

• Real-time collaboration is limited, as multiple users cannot edit reports simultaneously.
  
  
  
  

Best For

Businesses already using Microsoft 365 or Azure that need scalable, affordable, and collaborative BI with AI-driven insights.

Learn More: 5 Best Power BI Embedded Analytics Alternatives & Competitors 

4. Sisense: AI-Powered Analytics for Data Engineering Teams

Alt Text: Sisense home page

Sisense provides a flexible, scalable analytics platform that bridges the gap between complex data engineering workflows and actionable business intelligence. 

It enables teams to integrate, model, and analyze large datasets across multiple sources without building pipelines from scratch, making it ideal for organizations that need advanced analytics without extensive infrastructure overhead.

Key Features for Data Engineering

• AI-Driven Analytics: Infuse AI insights directly into dashboards and applications.
  
  
  

• Code-Free to Code-First Tools: Supports users across skill levels, from no-code analytics to advanced SQL or Python workflows.
  
  
  

• Data Connectivity: Seamlessly connect to cloud and on-premise sources.
  
  
  

• Embedded Analytics: Integrate dashboards and analytics directly into apps or portals.
  
  
  

• Multi-Tenant Support: Handle multiple clients or business units securely.
  
  
  

• Customizable Dashboards: Tailor dashboards to match your organization’s branding.
  
  
  

• Scheduled Reporting: Automate delivery of reports at defined intervals.
  
  
  

• Export Options: CSV, PDF, and other formats for offline analysis.
  
  
  

• Security & Governance: Enterprise-level compliance, single sign-on (SSO), and audit capabilities.

Pricing

Sisense follows a custom pricing model tailored to each organization’s requirements. 

Sisense pricing is not publicly listed, and interested teams need to contact Sisense to obtain a plan based on scale, data sources, and features needed.

Pros

• Sisense offers over 400 data connectors, enabling seamless integration of diverse data sources for comprehensive analysis.
  
  
  

• The platform's AI-driven features, such as natural language exploration and auto-generated summaries, accelerate insight generation for users at all technical levels.
  
  
  

• Sisense's Compose SDK allows developers to embed customizable analytics directly into applications, providing a tailored user experience. 

Cons

• Requires technical setup to fully leverage Compose SDK and embedded analytics features.
  
  
  

• Predictive and GenAI capabilities are limited to select enterprise-tier deployments.
  
  
  

• Natural language and auto-summary tools can struggle with complex or unstructured datasets.
  
  
  

• Dependent on vendor support for enterprise deployments
  
  
  

Best For

Data engineering and analytics teams seeking enterprise-grade, AI assisted analytics that integrate seamlessly into existing workflows, enabling faster insights without building custom pipelines from scratch.

 Learn More: 5 Best Sisense Competitors or Alternatives You Must Try In 2025

5. Airflow: Workflow Orchestration for Data Engineering

Alt Text: Airflow Home Page

Airflow is an open-source platform designed to programmatically author, schedule, and monitor complex data pipelines. 

It allows data engineers to orchestrate workflows, manage dependencies, and automate ETL processes across distributed systems.

Key Features for Data Engineering

• DAG-Based Workflows: Define workflows as Directed Acyclic Graphs for better dependency management.
  
  
  

• Scheduling & Automation: Automate pipeline execution with flexible scheduling options.
  
  
  

• Extensible Architecture: Integrate with multiple databases, cloud services, and custom operators.
  
  
  

• Monitoring & Logging: Track pipeline performance and quickly debug failed tasks.
  
  
  

• Scalable Execution: Run tasks in parallel across multiple workers for large-scale data processing.
  
  
  

• Integration with BI Tools: Easily connect outputs to analytics or reporting platforms.
  
  
  

• Custom Operators & Plugins: Extend functionality to meet specific workflow requirements.
  
  
  

• Dynamic Pipeline Generation: Generate workflows programmatically for complex or repetitive tasks.
  
  
  

• Version Control Integration: Maintain pipelines in Git for team collaboration and governance.
  
  
  

Pricing

• Open-Source: Free to use, with community support.
  
  
  

• Enterprise Deployments: Pricing varies if using managed Airflow services (e.g., Astronomer, Cloud Composer, or AWS Managed Workflows), contact providers for specific quotes.
  
  
  

Pros

• Highly flexible and customizable for complex data workflows.
  
  
  

• Open-source with a large community and extensive documentation.
  
  
  

• Scalable for both small pipelines and enterprise-scale ETL jobs.
  
  
  

• Integrates with most cloud providers and data platforms.
  
  
  

Cons

• Requires engineering expertise to set up and maintain.
  
  
  

• No native embedded dashboards; visualization requires external tools.
  
  
  

• Monitoring and alerting need configuration and may require additional tools.
  
  
  

Best For

Data engineering teams that need robust workflow orchestration, automation, and scalability for large-scale ETL pipelines, with full control over task dependencies and scheduling.

6. Snowflake: Cloud Data Platform for Modern Data Engineering

Alt Text: Snowflake Home page

Snowflake is a fully managed cloud data platform that enables organizations to centralize, store, and analyze structured and semi-structured data at scale. 

Its architecture separates compute from storage, making it ideal for data engineering workloads requiring elasticity, performance, and cost control.

Key Features for Data Engineering

• Separation of Compute & Storage: Scale each independently for cost efficiency.
  
  
  

• Multi-Cloud Support: Deploy across AWS, Azure, and Google Cloud.
  
  
  

• Data Sharing & Collaboration: Securely share data across teams or with external partners.
  
  
  

• Automatic Scaling: Handle large workloads with dynamic resource allocation.
  
  
  

• Time Travel & Fail-Safe: Restore data for disaster recovery or auditing.
  
  
  

• Semi-Structured Data Support: Native handling of JSON, Avro, ORC, and Parquet.
  
  
  

• Zero-Copy Cloning: Instantly replicate databases without storage overhead.
  
  
  

• Data Marketplace: Access third-party datasets directly.
  
  
  

• End-to-End Security: Always-on encryption and role-based access.
  
  
  

Pricing

• Standard: $2.00 per credit (USD)
  
  
  

• Enterprise: $3.00 per credit (USD)
  
  
  

• Business Critical: $4.00 per credit (USD)
  
  
  

• Virtual Private Snowflake (VPS): Custom pricing (Contact Sales)
  
  
  

Pros

• Sisense offers over 400 data connectors, enabling seamless integration of diverse data sources for comprehensive analysis.
  
  
  

• The platform's AI-driven features, such as natural language exploration and auto-generated summaries, accelerate insight generation for users at all technical levels. Sisense
  
  
  

• Sisense's Compose SDK allows developers to embed customizable analytics directly into applications, providing a tailored user experience.
  
  
  

Cons

• .The Elasticube™ engine requires technical expertise, often necessitating SQL scripting for effective data modeling.
  
  
  

• Dashboard customization is limited, with users needing JavaScript for advanced formatting and layout adjustments.
  
  
  

• Embedding analytics via iFrame restricts frontend customization, making seamless integration into existing applications challenging.

Best For

Enterprises and data engineering teams need a scalable, multi-cloud data warehouse with strong governance and high performance for analytics and pipeline workloads.

7. Google BigQuery: Serverless Data Warehouse for Enterprise Analytics

Alt Text: Google Bigquery Home Page

Google BigQuery is a fully managed, serverless data warehouse that simplifies large-scale analytics. Built for data engineering and AI workloads, it enables petabyte-scale queries, real-time streaming data, and seamless integration with Google Cloud services.

Key Features for Data Engineering

• Serverless Architecture: Eliminate infrastructure management and scale automatically.
  
  
  

• SQL-Based ML (BigQuery ML): Build and run machine learning models directly in SQL.
  
  
  

• Separation of Storage & Compute: Optimize performance and cost with independent scaling.
  
  
  

• Real-Time Analytics: Stream and analyze live data for immediate insights.
  
  
  

• Multi-Cloud & Open Source: Support for Apache Iceberg, Spark, and BigLake for hybrid workflows.
  
  
  

• Governance & Security: Data lineage, quality, and compliance features via Dataplex.
  
  
  

• AI Integration: Built-in Gemini AI for query assistance, anomaly detection, and automation.
  
  
  

• Disaster Recovery: Cross-region failover for mission-critical workloads.
  
  
  

Pricing

• Free Tier: 10 GB storage + 1 TB queries per month (on-demand) at no cost.
  
  
  

• Compute (On-Demand): Starting at $6.25 per TB scanned (first 1 TB free each month).
  
  
  

• Compute (Editions – Standard, Enterprise, Enterprise Plus): From $0.04 per slot hour, includes Gemini AI assistance.
  
  
  

• Storage (Logical): Starting at $0.01 per GB/month (uncompressed, modified within 90 days).
  
  
  

• Storage (Physical): Starting at $0.02 per GB/month (compressed, after 90+ days).
  
  
  

• Data Ingestion (Batch Loading):  Free.
  
  
  

• Streaming Inserts: $0.01 per 200 MB (first 2 TB/month free via Write API).
  
  
  

• Data Extraction (Batch Export): Free
  
  
  

• Streaming Reads (Read API): $1.10 per TB read.

Pros

• Handles petabyte-scale analytics with ease.
  
  
  

• Fully serverless, no infrastructure to manage.
  
  
  

• Strong AI/ML integration for advanced analytics.
  
  
  

• Cost-effective with flexible pricing options.
  
  
  

• Enterprise-grade governance and compliance.
  
  
  

Cons

• It charges based on the amount of data processed by queries, which can lead to unexpectedly high costs if queries are not optimized.
  
  
  

• It has a maximum query size limit of 1 TB per query, which may be restrictive for very large datasets.
  
  
  

• BigQuery does not support on-premises deployment, limiting its use to the Google Cloud Platform.
  
  
  

Best For

Enterprises and data engineering teams need scalable, real-time analytics and AI integration without the burden of managing infrastructure.

8. AWS Glue: Managed ETL & Data Engineering at Scale

Alt Text: AWS Glue Home page

AWS Glue is a fully managed extract, transform, and load (ETL) service designed for large-scale data engineering workflows. 

It enables engineers to discover, prepare, and combine data across multiple sources while minimizing infrastructure management, making it ideal for complex data pipelines and enterprise-scale analytics.

Key Features for Data Engineering

• Serverless ETL: Run Apache Spark, Spark Streaming, Ray (preview), or Python Shell jobs without managing infrastructure.
  
  
  

• Data Catalog: Centralized metadata repository with automated schema discovery and versioning.
  
  
  

• Interactive Sessions & Development Endpoints: Develop ETL scripts interactively with scalable DPU-based compute resources.
  
  
  

• Flexible Job Scheduling: Run jobs on demand, on a schedule, or trigger via events.
  
  
  

• DataBrew Integration: Visual, no-code data preparation for cleaning and transforming data.
  
  
  

• Scalable Processing: Pay only for the DPUs used per job, session, or endpoint, scaling compute automatically.
  
  
  

• Cross-Source Connectivity: Connect to Amazon S3, RDS, Redshift, and other sources seamlessly.
  
  
  

• Monitoring & Logging: Integration with CloudWatch for observability, logging, and anomaly detection.

Pricing

• Apache Spark / Spark Streaming Jobs: $0.44 per DPU-hour (US East, Ohio), billed per second with minimums.
  
  
  

• Memory Optimized R Workers (Spark 4.0): $0.52 per DPU-hour.
  
  
  

• Flexible Execution (Spark 3.0+): $0.29 per DPU-hour.
  
  
  

• Ray Jobs: $0.44 per M-DPU-hour.
  
  
  

• Python Shell Jobs: $0.44 per DPU-hour.
  
  
  

• Development Endpoints: $0.44 per DPU-hour, 10-minute minimum.
  
  
  

• Interactive Sessions: $0.44 per DPU-hour (standard) / $0.52 per DPU-hour (Memory Optimized R Workers), 1-minute minimum.
  
  
  

• Data Catalog & Quality: $0.44 per DPU-hour for ETL and anomaly detection.
  
  
  

Note: Additional charges apply for underlying AWS services used in ETL workflows (S3, RDS, Redshift, CloudWatch, etc.). Pricing varies by AWS region.

Pros

• Fully managed, serverless architecture removes infrastructure burden.
  
  
  

• Highly scalable for massive data volumes.
  
  
  

• Supports multiple ETL languages and frameworks.
  
  
  

• Tight integration with AWS ecosystem for end-to-end data engineering.
  
  
  

Cons

• Pricing can become complex for unpredictable workloads.
  
  
  

• Some advanced transformations may require Spark expertise.
  
  
  

• Monitoring and debugging large-scale jobs may require additional tools.
  
  
  

Best For

Data engineering teams managing large-scale ETL pipelines, needing serverless, scalable processing, and deep integration with AWS data sources for analytics and downstream AI/ML workloads.

9. Looker: Data Modeling & Enterprise Analytics Platform

Alt Text: Looker Home Page

Looker (Google Cloud core) is a data platform designed for enterprises to explore, model, and analyze large-scale datasets. 

Unlike traditional ETL-heavy data engineering workflows, Looker enables teams to build semantic models, create governed analytics, and deliver insights across the organization with minimal custom coding.

Key Features for Data Engineering

• Semantic Modeling: Create reusable, governed data models to standardize metrics across teams.
  
  
  

• Integrated Analytics: Connect to multiple data sources for real-time and historical analysis.
  
  
  

• Embedded Analytics: Deploy analytics into custom applications or external portals at scale.
  
  
  

• API Access: Automate queries, reporting, and integration with other enterprise systems.
  
  
  

• Multi-User Support: Manage Standard Users and Developer Users with controlled permissions.
  
  
  

• Governance & Security: Enterprise-grade access control, auditing, and monitoring for compliance.
  
  
  

• Scalable Querying: Supports high-volume, complex queries with optimized performance.
  
  
  

• Custom Dashboards & Reports: Build tailored dashboards and schedule automated reporting.

Pricing

• Starter – $200/mo: 10 users, 25 GB data, basic AI analytics.
  
  
  

• Standard – $825/mo: 25 GB data (expandable), AI insights, no-code automation.
  
  
  

• Premium – $2,750/mo: 50 GB data (expandable), predictive analytics, GenAI, guided onboarding.
  
  
  

• Enterprise – Custom: 250 GB+ data, multi-region support, advanced AI capabilities, personalized onboarding.

Pros

• Centralized data modeling reduces duplication and ensures metric consistency.
  
  
  

• Supports real-time and large-scale queries for enterprise datasets.
  
  
  

• Flexible embedded analytics for both internal and external stakeholders.
  
  
  

• Strong governance and security features for compliance-heavy organizations.
  
  
  

Cons

• Entry-level plans restrict API access and advanced developer controls.
  
  
  

• AI-powered features (Gemini) are still evolving, leading to occasional accuracy and reliability issues.
  
  
  

• Complex semantic modeling and LookML setup require technical expertise for full customization.
  
  
  

• Less suited for ad-hoc, low-code analytics for individual users.
  
  
  

Best For

Enterprises and data teams seek a governed, scalable analytics platform that integrates directly with data warehouses, supports semantic modeling, and enables both internal and external data-driven decision-making.

Learn  More: Looker Embedded Analytics: Is It Worth It In 2025?

10. Databricks: Unified Data Engineering

Alt Text: Databricks Home Page

Databricks offers a unified solution to ingest, transform, and orchestrate data, enabling teams to build reliable pipelines for analytics and AI without the complexity of managing multiple tools.

Key Features for Data Engineering

• Unified Tool Stack: Reduce costs and integration overhead with a single platform for collecting and cleaning data, including built-in governance and lineage.
  
  
  

• Streamlined ETL Development: No-code data connectors, declarative transformations, and AI-assisted code authoring speed up pipeline creation.
  
  
  

• Efficient Data Processing: Optimized engine for batch and low-latency real-time workloads, ensuring high performance and cost efficiency.
  
  
  

• Medallion Architecture: Automate movement from raw to refined data tables (bronze to gold), improving pipeline reliability.
  
  
  

• Scalable Job Runs: Support thousands of daily automated workflows for large-scale data orchestration.
  
  
  

Pricing

• Pay-as-you-go: Utilize Databricks services without upfront costs, paying only for the resources you consume, billed per second.
  
  
  

• Committed Usage Discounts: Opt for committed-use contracts to receive discounts and additional benefits, with the flexibility to apply commitments across multiple clouds..

Pros

• Accelerates data pipeline development and deployment.
  
  
  

• Reduces operational costs and integration complexity.
  
  
  

• Supports both batch and real-time processing efficiently.
  
  
  

• AI-assisted development simplifies coding tasks.
  
  
  

Cons

• Pricing is not publicly available and may be high for smaller teams.
  
  
  

• Real-time processing benefits require very large datasets.
  
  
  

• Advanced AI model deployment needs expert setup and governance.
  
  
  

• Generative AI workflows depend on maintaining full data lineage and privacy.
  
  
  

Best For

Enterprises and data teams seeking a scalable, end-to-end data engineering platform to manage ingestion, transformation, and orchestration with governance and AI-assisted productivity.

11. Talend: Data Integration & Engineering Platform

Alt Text: Talend home page

Talend (Qlik Talend Cloud) is an enterprise-grade platform for data engineering teams to integrate, transform, and govern data across cloud, on-prem, and hybrid environments.

It streamlines ETL/ELT pipelines, ensures data quality, and prepares datasets for analytics and AI workflows.

Key Features for Data Engineering

• Data Integration: Connect to SaaS apps, databases, lakes, and cloud warehouses.
  
  
  

• Data Transformation: Automated ELT/ETL pipelines with AI-augmented no-code transformations.
  
  
  

• Real-Time Data Movement: Log-based change data capture (CDC) for operational analytics.
  
  
  

• Data Governance & Quality: End-to-end lineage, semantic profiling, and stewardship workflows.
  
  
  

• Data Catalog & Marketplace: Discover datasets and ensure readiness for analytics and AI.
  
  
  

• Scalable Pipelines: Spark batch processing and adaptive ingestion for large data volumes.
  
  
  

• Application & API Integration: Seamless integration with enterprise apps and analytics tools.
  
  
  

• AI & Generative AI Support: Build pipelines and data products for AI workflows.
  
  
  

Pricing

• Starter – $200/mo: 10 users, 25 GB data, basic AI analytics.
  
  
  

• Standard – $825/mo: 25 GB data (expandable), AI insights, no-code automation.
  
  
  

• Premium – $2,750/mo: 50 GB data (expandable), predictive analytics, GenAI, guided onboarding.
  
  
  

• Enterprise – Custom: 250 GB+ data, multi-region support, advanced AI capabilities, personalized onboarding.
  
  
  

All plans include interactive dashboards, data source connectivity, and collaboration features.

Pros

• Unified platform simplifies end-to-end data engineering workflows.
  
  
  

• Scalable pipelines for large data volumes and complex transformations.
  
  
  

• Strong governance, quality, and AI readiness for enterprise use cases.
  
  
  

• Supports both batch and real-time data integration.
  
  
  

Cons

•   Talend requires Java CPU versions for compatibility; Java PSU versions may cause issues with Talend Studio.
  
  
  

• Users have reported slow performance and high CPU/memory usage, particularly during development.
  
  
  

• The enterprise version of Talend can be expensive for small businesses or startups, especially considering the additional costs for training and support.
  
  
  

Best For

Enterprises and data engineering teams seek a scalable, governed, and AI-ready platform to unify, transform, and prepare data for analytics, AI, and operational workflows.

12. ThoughtSpot: AI-Driven Analytics & Data Engineering Platform

Alt Text: ThoughtSpot Home page

ThoughtSpot is a data intelligence platform designed for enterprises to explore, analyze, and operationalize data at scale. 

Unlike traditional BI tools, ThoughtSpot enables data engineers and analytics teams to create AI-augmented dashboards, automate insights, and deliver governed analytics across the organization with minimal manual intervention.

Key Features for Data Engineering

• AI-Driven Analytics: Leverage AI agents to automatically generate insights, summaries, and recommendations from large-scale datasets.
  
  
  

• Dynamic Dashboards: Build interactive, real-time dashboards that scale with your data.
  
  
  

• Embedded Analytics: Integrate analytics into applications and external portals for seamless consumption.
  
  
  

• Automated Governance: Ensure data security, row-level access control, and compliance across all workflows.
  
  
  

• Data Connectivity: Connect natively to Snowflake, Databricks, Redshift, and other major data sources.
  
  
  

• Scalable Querying: Supports high-volume, complex queries with optimized performance for enterprise datasets.
  
  
  

• Developer Tools & APIs: Automate reporting, embed analytics, and integrate with custom applications.
  
  
  

• Mobile Support: Access insights anytime with iOS and Android support.
  
  
  

Pricing

ThoughtSpot Analytics

• Essentials: $25/user/month (billed annually)
  
  
  

• Pro: $50/user/month (billed annually)
  
  
  

• Enterprise: Custom pricing; unlimited users and data.

ThoughtSpot Embedded

• Developer: Free for 1 year, up to 10 users and 25M rows; includes dynamic embeddable AI dashboards, API & SDK.

• Enterprise (Embedded): Flexible pricing models aligned with your use case & GTM requirements

Pros

• Real-time AI insights with Spotter 3
  
  
  

• Embeddable analytics via Smart Apps
  
  
  

• Collaborative analysis in Analyst Studio
  
  
  

• Supports natural language queries for instant answers

Cons

• 
  ThoughtSpot’s embedded dashboards retain its UI, limiting full native integration in other apps.
  
  
  

• Its query-volume-based pricing can result in unpredictable and high costs for heavy usage.
  
  
  

• Visualization options are limited, offering less customization compared to competitors.

Best For

Data engineers and enterprise analytics teams seek AI-powered, scalable analytics with strong governance, multi-source connectivity, and embedded insights for both internal and external stakeholders.

Conclusion

Choosing the right data engineering tool is critical, but most platforms still leave teams dealing with disconnected workflows and uncertain data quality.

When observability and governance are missing, pipelines break silently, errors go undetected, and scaling becomes unreliable.

Upsolve simplifies this process by bringing everything into one platform:

• Real-time monitoring to identify and resolve pipeline issues early.
  
  
  

• Built-in compliance tracking to maintain governance without manual effort.
  
  
  

• Seamless integrations that connect data sources, pipelines, and teams efficiently.
  
  
  

Instead of managing separate tools for monitoring, automation, and governance, Upsolve centralizes them, making it easier to build, maintain, and scale data pipelines with full visibility and control.

Talk to us and explore a live demo to see how Upsolve can power your data engineering success.

FAQs

1. What are the best data engineering tools to use in 2025?

Top tools include Upsolve, Apache Spark, Power BI, Sisense, Airflow, Snowflake, Google BigQuery, AWS Glue, Looker, Databricks, Talend, and ThoughtSpot. They cover ETL, analytics, AI integration, orchestration, and governance.

2. How does Upsolve support modern data engineering?

Upsolve provides AI-driven observability, pipeline monitoring, and embedded analytics, helping data teams detect issues, track data quality, and deliver actionable insights efficiently.

3. When should teams use Apache Spark?

Use Apache Spark for distributed, large-scale data processing, real-time streaming, and complex ETL workflows requiring high performance across massive datasets.

4. How do Power BI and Sisense fit into data engineering workflows?

These BI tools visualize and analyze engineered datasets. Data engineers ensure clean, structured data is available for building dashboards and reporting.

5. Why is Apache Airflow important for data engineering?

Airflow orchestrates and schedules ETL workflows, ensuring pipelines run reliably and efficiently across multiple data sources.

6. What benefits do Snowflake and Google BigQuery provide?

They offer scalable, cloud-native data warehousing and querying, reducing infrastructure overhead and supporting high-volume, analytics-ready datasets.

7. What makes Databricks suitable for data engineers?

Databricks unifies data engineering, AI, and ML workflows, enabling teams to manage pipelines, ensure governance, and build generative AI models on structured and unstructured data.

8. How do AWS Glue and Talend simplify ETL processes?

These platforms automate extraction, transformation, and loading, integrating data from multiple sources while maintaining quality and scalability.

9. What is the difference between Looker and ThoughtSpot?

Looker focuses on semantic modeling and governed analytics, whereas ThoughtSpot provides AI-driven search and insights, enabling real-time decision-making across enterprise datasets.

10. Can Upsolve replace traditional BI tools?

Upsolve complements BI tools by offering embedded analytics, pipeline observability, and AI-driven insights. For SaaS teams, it helps deliver actionable metrics directly to end users without heavy engineering effort.