Big Data & Analytics Workloads
Big data and analytics workloads process, transform, and analyze massive datasets to generate insights for business, science, and AI. Unlike inference or HPC, which focus on latency or numerical precision, analytics workloads emphasize throughput, scale-out storage, and flexible query performance. They underpin data-driven decision making, business intelligence, and AI model preparation.
Overview
- Purpose: Collect, clean, transform, and analyze structured and unstructured data at scale.
- Scale: From terabytes to exabytes; thousands of concurrent queries and streaming events.
- Characteristics: ETL pipelines, distributed storage, batch and stream processing, SQL/NoSQL queries, BI dashboards.
- Comparison: Distinct from AI training (which consumes curated datasets) and from SaaS (which serves apps); analytics is about data pipelines and insight generation.
Common Workloads
- ETL / ELT Pipelines: Extract, transform, and load raw data into analytic-ready form.
- Data Lakes: Store unstructured and semi-structured data for flexible exploration.
- Data Warehousing: Structured, schema-driven analytics for BI reporting.
- Stream Processing: Real-time analytics of logs, IoT feeds, clickstreams.
- Business Intelligence: Dashboards and reporting systems used by enterprises.
Bill of Materials (BOM)
| Domain | Examples | Role |
|---|---|---|
| Storage | HDFS, Amazon S3, Google Cloud Storage, Azure Data Lake | Scalable object and distributed file storage |
| Compute | Apache Spark, Databricks, Presto/Trino, Flink | Batch and stream data processing engines |
| Databases | Snowflake, BigQuery, Redshift, Teradata | Analytical data warehouses with SQL interfaces |
| Stream Processing | Kafka, Pulsar, Kinesis | Capture and process real-time events |
| Orchestration | Airflow, dbt, Luigi | Coordinate ETL/ELT jobs and dependencies |
| Visualization | Tableau, Power BI, Looker | Generate reports and dashboards for decision makers |
Facility Alignment
| Workload Mode | Best-Fit Facilities | Also Runs In | Notes |
|---|---|---|---|
| Data Lakes | Hyperscale | Enterprise DCs | Large-scale object storage, global access |
| Data Warehousing | Hyperscale | Colocation | Elastic compute + SQL analytics |
| ETL Pipelines | Enterprise DCs, Colo | Hyperscale | Hybrid common due to data gravity |
| Streaming Analytics | Edge + Metro Colo | Enterprise | IoT and clickstream ingestion |
Key Challenges
- Data Gravity: Moving petabytes between clouds/DCs is costly and slow.
- Latency: Streaming workloads demand sub-second insights; batch jobs tolerate hours.
- Complexity: Managing hybrid pipelines across clouds, colos, and enterprise estates.
- Security: Data governance and compliance (GDPR, HIPAA, SOC 2) are critical.
- Cost: Storage + compute scaling can become unpredictable without FinOps practices.
Notable Deployments
| Deployment | Operator | Scale | Notes |
|---|---|---|---|
| Snowflake Data Cloud | Snowflake | 10k+ enterprises | Elastic data warehouse SaaS |
| Google BigQuery | Google Cloud | Exabyte-scale | Serverless analytics platform |
| Databricks Lakehouse | Databricks | Global deployments | Unified data lake + warehouse analytics |
| Cloudera Data Platform | Cloudera | Hybrid enterprises | Legacy Hadoop evolved into hybrid data ops |
| Palantir Foundry | Palantir | Governments + enterprises | Data fusion, analytics, and compliance-heavy environments |
Future Outlook
- Lakehouse Adoption: Convergence of data lakes and warehouses into unified architectures.
- Real-Time Analytics: Streaming-first architectures for IoT, finance, and security.
- AI Integration: Analytics pipelines directly feeding AI/ML model training.
- Data Sovereignty: Localized data lakes to comply with regional regulations.
- FinOps Practices: Increasing focus on cost optimization in cloud-based analytics.
FAQ
- How do analytics workloads differ from AI training? Analytics transforms and queries data; AI training consumes datasets to optimize models.
- Where do analytics workloads run? Hyperscale clouds, hybrid colos, and enterprise data centers.
- Are analytics workloads latency-sensitive? Streaming analytics are; batch ETL and BI reporting are not.
- Why are analytics workloads costly? Storage + compute scaling unpredictably with data growth and query complexity.
- What’s next? Real-time AI-assisted analytics and lakehouse convergence.