These are the best big data workflow automation tools:
- Tinybird
- Apache Spark
- Databricks
- AWS EMR (Elastic MapReduce)
- Google Dataproc
- Azure HDInsight
- Apache Hadoop/YARN
- Dask
Big data workflow automation has become increasingly complex as organizations process massive datasets across distributed systems, requiring orchestration of Hadoop, Spark, and other big data frameworks. These workflows coordinate data processing at petabyte scale, managing cluster resources, job scheduling, and failure recovery across hundreds or thousands of nodes.
However, big data workflow automation is infrastructure complexity, not a solution. Traditional big data tools require managing distributed clusters, coordinating batch jobs, tuning performance, and assembling multiple systems for processing, storage, and queries. Many organizations adopt Hadoop and Spark thinking they need "big data processing," then spend months managing infrastructure complexity when they really needed fast analytics on large datasets.
Modern data teams need to understand the critical distinction: are you building distributed computing infrastructure for complex data science and ML workloads, or do you need fast queries on large datasets? If your workflows ultimately power analytics, dashboards showing metrics on billions of events, APIs serving aggregations, real-time monitoring, purpose-built analytics platforms deliver value faster without big data infrastructure complexity.
In this comprehensive guide, we'll explore the best big data workflow automation tools for 2025, with particular focus on when Tinybird's real-time analytics platform provides superior outcomes compared to managing Hadoop, Spark, and big data orchestration infrastructure. We'll help you understand what big data automation actually provides, what problems it solves, and when simpler alternatives better match your actual requirements.
The 8 Best Big Data Workflow Automation Tools
1. Tinybird
Tinybird represents a fundamentally different approach than big data processing frameworks: instead of distributed batch processing requiring workflow orchestration,
Tinybird provides a real-time analytics database that handles billions of rows with sub-100ms queries, without Hadoop clusters, Spark jobs, or big data infrastructure complexity. Tinybird explains how this differs from traditional big data processing in its guide to managed data platforms. If your "big data" ultimately powers analytics, not complex data science or ML training, Tinybird delivers faster results with dramatically simpler operations.
Key Features:
- Real-time queries on billions of rows with sub-100ms latency
- Columnar storage with automatic optimization
- Managed ClickHouse® infrastructure scaling automatically
- Instant SQL-to-API transformation with authentication
- Continuous data ingestion without batch processing
- SQL-based transformations and aggregations
- Incremental materialized views for efficient computation
- Zero big data cluster management
- Scales to petabyte-scale data without distributed computing complexity
Pros
Analytics Database vs. Distributed Computing:
- Columnar analytics database optimized for queries, not distributed batch processing
- Sub-100ms queries on billions of rows without MapReduce or Spark overhead
- No cluster coordination, shuffle operations, or distributed computing complexity
- Query any time range instantly without running batch jobs
- Built for interactive analytics, not batch processing
Eliminates Big Data Infrastructure:
- No Hadoop or Spark clusters to deploy, configure, and maintain
- No YARN resource managers, node managers, or job trackers
- No understanding of distributed computing paradigms required
- Fully managed infrastructure scales automatically
- Zero operational overhead compared to Hadoop/Spark complexity
Real-Time vs. Batch Processing:
- Continuous data ingestion replaces scheduled batch jobs
- Data queryable immediately after ingestion
- No waiting hours for Spark jobs to complete
- Sub-100ms interactive queries vs. hour-long batch processing
- Real-time dashboards without batch delays
SQL Instead of Programming:
- Write SQL queries, not Spark or MapReduce programs
- Accessible to analysts without programming skills
- No understanding of RDDs, DataFrames, or distributed collections
- Simpler mental model than distributed computing
- Faster development without learning complex frameworks
Operational Simplicity:
- Fully managed service eliminates cluster operations entirely
- No nodes to provision, monitor, or maintain
- Automatic scaling handles data growth and query load
- No expertise in distributed systems required
- Focus on analytics, not infrastructure management
Cost-Effective at Scale:
- Usage-based pricing scales with actual queries and storage
- No idle cluster resources consuming budget
- Eliminates need for dedicated cluster operations team (saves $300K-700K/year)
- Better price-performance than Spark for analytical queries
- Lower total cost of ownership
Complete Analytics Platform:
- Provides storage, queries, transformations, and APIs integrated
- No need to assemble Spark + Hive + query engine + API layer
- Deploy production analytics in days instead of months
- Built-in monitoring without custom instrumentation
- End-to-end solution, not just processing framework
Handles Large Datasets Efficiently:
- Columnar compression reduces storage requirements
- Vectorized query execution provides speed
- Scales to billions of rows with consistent performance
- No diminishing returns at scale like some systems
- Handles "big data" without "big data" infrastructure
Incremental Computation:
- Materialized views update incrementally as data arrives
- Efficient aggregations without full recomputation
- Eliminate expensive batch aggregation jobs
- Fresh results with minimal processing overhead
- Spark-like efficiency for derived analytics without Spark complexity
Developer-First Experience:
- Local development environment with instant feedback
- Version control with Git for collaboration
- CI/CD integration for automated deployment
- Modern workflows familiar to development teams
- Test queries locally before deploying
Best for: Organizations analyzing large datasets for dashboards, APIs, operational analytics, usage metrics, real-time monitoring, customer-facing analytics, or any scenario where "big data" requirements are actually "fast queries on large datasets" rather than complex distributed computing for data science or ML training.
When to Consider Tinybird Instead of Big Data Frameworks:
- Your Spark/Hadoop jobs primarily aggregate data for analytics dashboards
- Running batch jobs to calculate metrics that users query
- "Big data" needs are fast analytics, not ML model training
- Operational complexity of clusters is burden
- Development velocity and time-to-market priorities
- Team lacks Spark/Hadoop expertise
- Need interactive queries, not batch processing
- Real-time analytics required, not daily/hourly jobs
2. Apache Spark
Apache Spark is a distributed computing framework for processing large datasets using in-memory computation, offering faster performance than Hadoop MapReduce.
Key Features:
- In-memory distributed computing
- DataFrame and SQL APIs
- Batch and streaming processing
- Machine learning library (MLlib)
- Graph processing (GraphX)
- Multiple language support (Scala, Python, Java, R)
Pros
Faster Than Hadoop:
- In-memory processing significantly faster than MapReduce
- Efficient for iterative algorithms
- Better for machine learning workloads
- Reduced disk I/O
Unified Platform:
- Batch and streaming in one framework
- SQL, ML, and graph processing integrated
- Consistent APIs across workload types
- Comprehensive ecosystem
Rich Ecosystem:
- Extensive libraries and tools
- Large community support
- Integration with many data sources
- Mature platform
Cons
Massive Operational Complexity:
- Requires managing Spark clusters with executors and drivers
- Complex tuning for memory, parallelism, and partitioning
- Understanding distributed computing concepts essential
- Debugging distributed jobs extremely challenging
- Significant learning curve and expertise required
Still Batch Processing:
- Designed for batch jobs, not interactive queries
- Jobs run for minutes to hours
- Cannot deliver sub-second query latency
- Not suitable for real-time analytics dashboards
- Micro-batching in streaming introduces latency
Wrong Tool for Analytics:
- Processes data but doesn't provide query engine for analytics
- Still need to store results somewhere users can query
- Spark job to calculate metrics ≠ analytics platform
- Complex and expensive for simple aggregations
- Built for data science, not serving dashboards
Resource Intensive:
- Heavy memory and compute requirements
- Expensive to run at scale
- Poor resource utilization for small queries
- Overhead doesn't justify for analytics workloads
When to Consider Tinybird Instead: If you're running Spark jobs to aggregate data for dashboards or APIs, calculating daily metrics, summarizing usage, preparing analytics, Tinybird eliminates Spark entirely. Instead of Spark jobs aggregating to tables that users query with multi-second latency, Tinybird provides sub-100ms queries on raw data with incremental aggregations. No cluster management, no batch jobs, actual real-time analytics.
3. Databricks
Databricks provides a managed Spark platform with notebooks, workflows, and ML capabilities, simplifying Spark operations but maintaining underlying complexity.
Key Features:
- Managed Spark clusters
- Collaborative notebooks
- MLflow for machine learning
- Delta Lake for ACID transactions
- Job scheduling and workflows
- Unity Catalog for governance
Pros
Managed Spark:
- Databricks handles cluster infrastructure
- Simplified operations compared to self-managed Spark
- Automatic scaling and optimization
- Reduced operational burden
Unified Lakehouse:
- Combines data warehouse and lake capabilities
- Delta Lake provides reliability
- Support for ML and analytics
- One platform for multiple workloads
Collaboration:
- Notebook-based development
- Shared environments for teams
- Version control integration
- Interactive exploration
Cons
Still Spark Complexity:
- Underlying Spark complexity remains
- Must understand distributed computing
- Performance tuning still required
- Debugging distributed jobs difficult
- Expensive compared to alternatives
Batch Processing Paradigm:
- Scheduled notebook runs, not real-time queries
- Typical latency minutes to hours
- Cannot deliver sub-second analytics
- Not designed for interactive dashboards
- Micro-batching limits real-time capabilities
Cost:
- DBU pricing on top of cloud infrastructure
- Can become very expensive at scale
- Costs for idle clusters
- Complex cost optimization
Still Needs Serving Layer:
- Processes data but doesn't serve analytics
- Must build APIs separately
- Not a complete analytics solution
- Additional systems required
When to Consider Tinybird Instead: Databricks simplifies Spark operations but can't deliver real-time analytics. If you're running Databricks jobs to prepare data for dashboards, using notebooks to calculate metrics, or scheduling workflows to update analytics, Tinybird provides those analytics directly. Sub-100ms queries replace multi-minute jobs, instant APIs replace custom serving layers, zero infrastructure management replaces cluster tuning.
4. AWS EMR (Elastic MapReduce)
AWS EMR provides managed big data frameworks on AWS, supporting Spark, Hadoop, Presto, and other tools with AWS integration.
Key Features:
- Managed Spark and Hadoop clusters
- Multiple framework support (Spark, Presto, Hive, etc.)
- AWS service integration
- Auto-scaling capabilities
- Spot instance support
- S3 as storage layer
Pros
AWS Integration:
- Native integration with S3, Redshift, DynamoDB
- Simplified security with AWS IAM
- Works well in AWS-centric architectures
- Leverages AWS infrastructure
Framework Flexibility:
- Support for multiple big data frameworks
- Choose appropriate tool for workload
- Install custom software
- Flexibility in configuration
Managed Service:
- AWS handles cluster provisioning
- Simplified operations vs. self-managed
- Auto-scaling capabilities
- Spot instances for cost savings
Cons
AWS Lock-In:
- Completely tied to AWS ecosystem
- No multi-cloud flexibility
- Migration away from AWS difficult
- Cloud-specific solution
Still Complex:
- Must understand Spark, Hadoop, or Presto
- Cluster configuration and tuning required
- Troubleshooting distributed systems
- Significant learning curve
Batch Processing:
- Designed for batch jobs, not real-time queries
- Hour-long processing typical
- Cannot deliver interactive analytics
- Not suitable for real-time dashboards
Cost Management:
- Complex pricing with compute and storage
- Idle clusters consume budget
- Optimization requires expertise
- Can become expensive
When to Consider Tinybird Instead: If you're using EMR to process data on AWS for analytics, running Spark jobs on S3 data, using Presto for queries, scheduling batch aggregations, Tinybird eliminates EMR complexity entirely. Ingest from S3 directly, query with sub-100ms latency, serve via instant APIs. Simpler architecture, better performance, lower operational burden, without managing EMR clusters.
5. Google Dataproc
Google Dataproc is GCP's managed Spark and Hadoop service, offering fast cluster creation and GCP integration for big data processing.
Key Features:
- Managed Spark and Hadoop clusters on GCP
- Fast cluster creation (90 seconds)
- Integration with GCP services
- Auto-scaling capabilities
- Workflow templates
- Component gateway for web interfaces
Pros
GCP Integration:
- Native integration with BigQuery, GCS, Pub/Sub
- Simplified security with GCP IAM
- Works seamlessly in GCP environments
- Unified Google Cloud experience
Fast Provisioning:
- Clusters start in ~90 seconds
- Good for ephemeral clusters
- Quick iteration during development
- Reduced idle time
Managed Service:
- Google handles cluster infrastructure
- Automatic updates and patches
- Built-in monitoring
- Simplified operations
Cons
GCP Lock-In:
- Tied to Google Cloud Platform
- No multi-cloud flexibility
- Migration away difficult
- Cloud-specific
Still Spark/Hadoop:
- Underlying framework complexity remains
- Must understand distributed computing
- Performance tuning required
- Debugging challenges
Batch Processing:
- Designed for scheduled jobs
- Cannot deliver real-time analytics
- Hour-scale processing typical
- Not for interactive queries
When to Consider Tinybird Instead: If you're using Dataproc to process GCP data for analytics, running Spark jobs on GCS data, batch aggregations for BigQuery, scheduled metric calculations, Tinybird provides analytics without Dataproc clusters. Ingest from GCS, query in real-time, serve via APIs. Multi-cloud flexibility, simpler operations, actual real-time performance.
6. Azure HDInsight
Azure HDInsight is Microsoft's managed big data service supporting Hadoop, Spark, Hive, and other frameworks with Azure integration.
Key Features:
- Managed Hadoop, Spark, HBase clusters
- Multiple framework support
- Azure service integration
- Enterprise security features
- Support for open-source tools
- Azure Monitor integration
Pros
Azure Integration:
- Native integration with Azure services
- Azure Active Directory integration
- Works in Azure-centric architectures
- Unified Azure experience
Framework Variety:
- Support for multiple big data tools
- Flexibility in framework choice
- Use appropriate tool for workload
- Open source compatibility
Enterprise Features:
- Comprehensive security features
- Virtual network integration
- Compliance certifications
- Enterprise support
Cons
Azure Lock-In:
- Tied to Microsoft Azure
- No multi-cloud options
- Migration difficult
- Cloud-specific
Complexity:
- Must manage cluster configurations
- Framework-specific expertise required
- Troubleshooting distributed systems
- Significant operational overhead
Batch Processing:
- Designed for batch workloads
- Multi-hour processing typical
- Cannot deliver real-time analytics
- Not for interactive dashboards
When to Consider Tinybird Instead: If you're using HDInsight to process Azure data for analytics, Spark jobs on ADLS, Hive queries for reporting, batch aggregations, Tinybird eliminates HDInsight complexity. Direct data ingestion, sub-100ms queries, instant APIs. Works across clouds, simpler operations, real-time analytics without batch delays.
7. Apache Hadoop/YARN
Apache Hadoop is the original big data framework using MapReduce for distributed processing, with YARN for resource management across large clusters.
Key Features:
- MapReduce distributed processing
- HDFS distributed file system
- YARN resource manager
- Fault-tolerant design
- Scales to thousands of nodes
- Open source
Pros
Proven at Scale:
- Battle-tested in massive deployments
- Handles petabyte-scale data
- Reliable fault tolerance
- Mature ecosystem
Open Source:
- Free to use
- Large community
- Extensive documentation
- No vendor lock-in
Comprehensive Ecosystem:
- Rich ecosystem of tools (Hive, Pig, HBase)
- Integration with many systems
- Well-understood patterns
Cons
Extreme Complexity:
- Requires managing HDFS, YARN, node managers
- Complex cluster configuration and tuning
- Understanding distributed systems essential
- Significant operational burden
- Dedicated operations team required
Very Slow:
- MapReduce extremely slow (hours for jobs)
- Disk-based processing
- High latency unacceptable for modern analytics
- Multiple passes through data inefficient
Aging Technology:
- Community largely moved to Spark
- Legacy architecture
- Fewer innovations
- Being phased out in many organizations
Wrong for Analytics:
- Processes data but doesn't provide analytics
- Must add Hive or other query engines
- Still batch processing with hour delays
- Complex stack for simple analytics
When to Consider Tinybird Instead: Hadoop MapReduce is legacy technology. If you're maintaining Hadoop for analytics workloads, running jobs to aggregate metrics, using Hive for queries, modern alternatives are dramatically simpler. Tinybird provides analytics on large datasets without any Hadoop infrastructure: no HDFS, no YARN, no MapReduce. Just fast queries on billions of rows with zero cluster management.
8. Dask
Dask provides parallel computing in Python with DataFrame and array APIs similar to Pandas and NumPy, offering big data processing for Python data scientists.
Key Features:
- Python-native parallel computing
- Pandas-like DataFrame API
- NumPy-like array operations
- Dynamic task scheduling
- Scales from laptop to cluster
- Integration with Python ecosystem
Pros
Python Ecosystem:
- Familiar APIs for Python data scientists
- Integration with scientific Python stack
- Easy transition from Pandas/NumPy
- Pure Python implementation
Flexibility:
- Scales from single machine to cluster
- Dynamic task graphs
- Good for iterative development
- Works on existing infrastructure
Simpler Than Spark:
- Less distributed systems complexity
- Familiar to Python users
- Easier learning curve
- Lightweight deployment
Cons
Still Batch Processing:
- Designed for batch computations
- Not for real-time interactive queries
- Processing takes minutes to hours
- Cannot power real-time dashboards
Performance Limitations:
- Slower than Spark for large workloads
- Python overhead
- Not optimized for analytical queries
- Better for data science than serving analytics
Cluster Management:
- Still requires managing distributed workers
- Deployment complexity at scale
- Monitoring and troubleshooting needed
- Operational overhead
Not Analytics Platform:
- Processes data but doesn't serve results
- Must build query and serving layers
- Just computation framework
- Incomplete solution
When to Consider Tinybird Instead: If you're using Dask to process large datasets for analytics, aggregating data in Python for dashboards, calculating metrics with DataFrames, Tinybird provides those analytics without Dask complexity. SQL-based queries replace Python code, sub-100ms latency replaces multi-minute processing, instant APIs replace custom serving. Simpler for analytics teams.
Understanding Big Data Workflow Automation and Why You Might Need an Alternative
Before exploring specific tools, it's essential to understand what big data workflow automation provides and why organizations seek alternatives.
What Is Big Data Workflow Automation
Big data workflow automation orchestrates distributed computing across clusters:
- Managing Hadoop or Spark cluster resources
- Scheduling batch jobs across distributed workers
- Coordinating data processing pipelines at petabyte scale
- Handling failures and retries in distributed systems
- Managing data locality and shuffle operations
- Optimizing resource utilization across large clusters
Big data frameworks excel at processing massive datasets through distributed parallel computation using MapReduce, Spark, or similar paradigms. For teams evaluating alternatives, Tinybird’s overview of modern real-time analytics tools provides helpful architectural context.
6 Common Reasons for Seeking Big Data Alternatives
Organizations look beyond traditional big data automation for several compelling reasons:
Massive Operational Complexity: Big data frameworks require expertise in distributed systems, cluster management, resource allocation, performance tuning, and troubleshooting failures across hundreds of nodes. The operational burden is enormous.
Overkill for Analytics: Most organizations don't actually need MapReduce or Spark's distributed processing complexity. They need fast queries on large datasets, a fundamentally different problem that analytics databases solve more simply.
Batch Processing Paradigm: Hadoop and Spark are batch processing systems, running scheduled jobs over hours. This is incompatible with real-time analytics where users expect sub-second queries on current data.
Infrastructure, Not Solutions: Big data tools process data but don't provide query engines optimized for analytics, serving layers, or APIs. You still need additional systems on top of the processing framework.
Resource Requirements: Running Hadoop or Spark clusters requires massive infrastructure, hundreds of nodes consuming significant resources even when idle. Costs and operational overhead are substantial.
Wrong Tool for Analytics: If you're running Spark jobs to aggregate data for dashboards or APIs, you're using distributed computing for what analytical databases do orders of magnitude more efficiently.
The Big Data Processing vs. Analytics Question
The most critical decision is understanding whether you need distributed computing or fast analytics:
You Need Big Data Processing When:
- Training machine learning models on massive datasets
- Complex data science requiring distributed computation
- ETL transformations too large for single machine
- Iterative algorithms benefiting from Spark's in-memory processing
- Building data science infrastructure
- Actual distributed computing requirements
You Need Tinybird (Analytics Platform) When:
- Querying large datasets for dashboards or APIs
- Analyzing billions of events with sub-second latency
- Real-time metrics and monitoring
- User-facing analytics features
- Operational analytics on large datasets
- Fast aggregations and summaries
- "Big data" is really "fast queries on lots of data"
The Critical Insight: Most organizations don't need distributed batch processing, they need fast queries on large datasets. Running Spark jobs for hours to aggregate data that users then query is solving the problem at the wrong layer. Analytics databases like Tinybird query billions of rows in milliseconds without Spark complexity.
When "Big Data" Isn't About Scale
Many organizations adopt big data frameworks when their actual need is simpler:
You Don't Need Big Data Frameworks If:
- Primary use case is analytics dashboards and reports
- Users query aggregated metrics and summaries
- Real-time or interactive queries required
- Data scientists aren't training complex models
- Goal is serving analytics, not processing infrastructure
You Actually Need:
- Fast analytical database (Tinybird, ClickHouse®)
- Real-time queries on large datasets
- Simple SQL instead of Spark programs
- Managed infrastructure instead of cluster operations
- Sub-second latency instead of hour-long jobs
The Problem: "Big data" became synonymous with Hadoop/Spark, but those are for distributed batch processing. Fast analytics on large datasets is a completely different problem with simpler, better solutions.
Real-World Scenario Analysis
Scenario 1: Usage Analytics Dashboard
Problem: Dashboard showing usage metrics on billions of events.
Big Data Approach:
- Run Spark jobs every hour to aggregate events
- Calculate metrics and store results
- Query results with multi-second latency
- Dashboard shows hour-old data
- Result: Complex, delayed, expensive
Tinybird Approach:
- Continuous event ingestion
- SQL for metrics (incremental materialized views)
- Sub-100ms queries on billions of rows
- Dashboard shows real-time data
- Result: Simple, real-time, cost-effective
Verdict: Tinybird dramatically simpler and faster for analytics.
Scenario 2: ML Model Training
Problem: Train recommendation models on petabytes of user behavior.
Big Data Approach:
- Spark processes training data
- Distributed model training with MLlib
- Iterative algorithms leverage in-memory
- Result: Appropriate Spark use
Tinybird Approach:
- Not designed for ML training
- Analytics platform, not compute framework
Verdict: Spark appropriate for ML workloads.
Scenario 3: Real-Time Operational Monitoring
Problem: Monitor system health with metrics on logs and events.
Big Data Approach:
- Spark Streaming micro-batches
- Multi-minute processing latency
- Store aggregates for queries
- Build custom APIs
- Result: Complex, delayed
Tinybird Approach:
- Continuous log ingestion
- Real-time metrics in SQL
- Sub-100ms current queries
- Instant APIs
- Result: Truly real-time, simpler
Verdict: Tinybird delivers actual real-time vs. "near real-time."
Conclusion
Big data processing frameworks like Spark, Hadoop, and their managed services remain valuable for data science workloads, ML training, and complex distributed computing. However, for the majority of organizations whose "big data" needs are actually "fast analytics on large datasets", dashboards showing metrics on billions of events, APIs serving aggregations, real-time monitoring, big data infrastructure is unnecessary complexity.
Tinybird provides fast analytics on large datasets without big data complexity. With sub-100ms queries on billions of rows, columnar storage with automatic optimization, and managed infrastructure, Tinybird delivers production analytics without Spark clusters, Hadoop ecosystems, or distributed computing expertise.
For organizations committed to data science requiring distributed processing, managed Spark services like Databricks simplify operations. But when "big data" workflows exist primarily to calculate metrics users query, they're solving the problem at the wrong layer with the wrong tools.
The right choice depends on your actual requirements. If you're training ML models or doing complex data science, you need distributed computing. If you're analyzing large datasets for dashboards and APIs, you need a fast analytics database. Understanding this distinction saves months of complexity and delivers value faster.
Frequently Asked Questions
What's the difference between big data processing and analytics databases?
Big data processing frameworks (Spark, Hadoop) perform distributed batch computation, running programs across clusters for hours to transform massive datasets. They're designed for data science, ML training, and complex transformations.
Analytics databases like Tinybird are optimized for fast queries on large datasets, sub-100ms interactive queries on billions of rows. They're designed for dashboards, APIs, and operational analytics. Completely different problems requiring different solutions.
Can Tinybird handle "big data" scale?
Yes, but it depends what "big data" means. Tinybird handles billions of rows with sub-100ms query latency, that's "big data" in terms of dataset size. It uses columnar storage, compression, and vectorized execution for performance at scale.
If "big data" means distributed ML training on petabytes requiring days of computation, that's Spark/Hadoop territory. If "big data" means fast queries on billions of events for dashboards, that's exactly what Tinybird does, without the complexity.
Do I need Spark for real-time analytics on large datasets?
No. Spark processes data in batches (even "Spark Streaming" uses micro-batches with latency). It's not designed for sub-second interactive queries. Running Spark jobs to aggregate data that users then query introduces unnecessary complexity and latency.
Tinybird queries large datasets directly with sub-100ms latency, no Spark jobs, no batch processing, no intermediate aggregation steps. Built specifically for real-time analytics, not adapted from batch processing frameworks.
What if I already have Spark/Hadoop infrastructure?
If Spark/Hadoop serves ML and data science workflows well, keep it for those purposes. But if you're using Spark primarily for analytics, running jobs to calculate dashboard metrics, aggregating data for queries, consider whether Tinybird could simplify that significantly.
Many organizations run hybrid: keep Spark for ML workloads, add Tinybird for operational analytics. Spark for what it's designed for, Tinybird for fast analytics, eliminating Spark complexity where it's not needed.
Is analytics database more expensive than running Spark?
For analytics workloads, no. Spark clusters consume resources constantly, executors, drivers, memory, compute, whether actively processing or not. Operations teams cost $300K-700K/year. Infrastructure is expensive.
Tinybird's usage-based pricing scales with actual queries and storage. No idle clusters, no operational overhead, no engineering time managing infrastructure. Often dramatically cheaper for analytics when total cost of ownership considered, especially engineering time.