Core Big Data Frameworks & Platforms
| Technology | Description | Key Features | Best For |
|---|---|---|---|
| Apache Hadoop | Open-source distributed computing framework | • HDFS (storage) • YARN (resource management) • MapReduce (processing model) • Fault tolerance | Batch processing of large datasets; foundation for data lakes |
| Apache Spark | Fast, in-memory data processing engine | • Up to 100x faster than Hadoop MapReduce • Unified API for batch/stream processing • Built-in libraries for ML, graph processing, SQL | Interactive queries, machine learning, real-time analytics |
| Cloudera CDP | Enterprise data platform | • Integrated security & governance • Hybrid cloud architecture • End-to-end data lifecycle | Enterprise-scale deployments with compliance requirements |
| Databricks | Unified analytics platform | • Optimized Spark runtime • Notebook interface • MLflow integration • Delta Lake support | Collaborative data science, ML workflows, data engineering |
| Google BigQuery | Serverless data warehouse | • Separation of storage and compute • ML integration • Real-time analytics | Ad-hoc analysis of massive datasets without infrastructure management |
| Snowflake | Cloud data platform | • Multi-cluster architecture • Separation of storage/compute • Semi-structured data support | Enterprise data warehousing, data sharing, data applications |
| Amazon EMR | Managed Hadoop framework | • Easy cluster provisioning • Integration with AWS services • Support for Spark, Hive, Presto | Cloud-based big data processing on AWS |
Distributed Storage Technologies
| Technology | Type | Description | Best For |
|---|---|---|---|
| HDFS | Distributed file system | Hadoop’s storage layer with data replication | Storing large files for batch processing |
| Amazon S3 | Object storage | Highly durable cloud storage service | Cost-effective storage of any amount of data |
| Apache HBase | NoSQL columnar database | Random, real-time read/write access on HDFS | Real-time read/write access to sparse data |
| Apache Cassandra | NoSQL distributed database | Linear scalability with no single point of failure | Distributed applications requiring high availability |
| MongoDB | Document database | Flexible schema with BSON document storage | Applications with complex, evolving data models |
| Google Bigtable | NoSQL database service | Low latency and high throughput | Time-series data, IoT, analytics storage |
| Apache Parquet | Columnar file format | Efficient compression and encoding schemes | Analytical workloads with complex queries |
| Apache ORC | Columnar file format | Optimized for Hive with advanced compression | Reducing storage footprint for Hadoop workloads |
| Apache Iceberg | Table format | Schema evolution, atomic operations | Managing large analytic datasets |
| Delta Lake | Storage layer | ACID transactions on data lakes | Reliable data lakes with schema enforcement |
Data Processing Engines
| Technology | Processing Model | Key Features | Ideal For |
|---|---|---|---|
| MapReduce | Batch | • Simple programming model • High fault tolerance • Built for very large datasets | Batch jobs with linear data processing flow |
| Apache Spark Core | Batch & micro-batch | • In-memory processing • DAG execution engine • Lazy evaluation | General-purpose data processing |
| Apache Tez | DAG-based | • Dynamic DAG optimization • Container reuse • Resource-aware scheduling | Complex multi-stage data processing workflows |
| Apache Flink | Stream & batch | • True streaming (not micro-batch) • Event time processing • Exactly-once semantics | Applications requiring real-time processing with low latency |
| Presto/Trino | Distributed SQL | • MPP (massively parallel processing) • In-memory processing • Multiple data source connectors | Interactive querying across multiple data sources |
| Databricks Photon | Vectorized engine | • Vectorized execution • Native code generation • Compatible with Spark APIs | Performance-critical Spark workloads |
| Apache Beam | Unified (batch & stream) | • Runner agnostic • Unified programming model • Windowing abstractions | Portable data processing pipelines |
Stream Processing Technologies
| Technology | Description | Key Capabilities | Use Cases |
|---|---|---|---|
| Apache Kafka | Distributed event streaming platform | • High throughput • Scalable storage • Client libraries | Log aggregation, messaging, activity tracking |
| Kafka Streams | Client library for Kafka | • Exactly-once processing • No separate cluster required • Local state stores | Stream processing directly on Kafka |
| Apache Flink | Stream processing framework | • Stateful stream processing • Event time processing • High throughput/low latency | Complex event processing, anomaly detection |
| Spark Structured Streaming | Stream processing on Spark | • Incremental query execution • End-to-end exactly-once • Unified batch/streaming API | Integrating streaming with batch workloads |
| AWS Kinesis | Managed streaming service | • Automatic scaling • Integration with AWS services • Real-time analytics | Real-time dashboards, analytics on streaming data |
| Google Dataflow | Managed stream/batch processing | • Autoscaling • Unified batch/stream • Apache Beam implementation | Processing data pipelines with varying traffic |
| Azure Event Hubs | Cloud messaging service | • Big data streaming • Millions of events per second • Time retention | Telemetry intake, live dashboarding |
Query & Analysis Tools
| Technology | Type | Key Features | When To Use |
|---|---|---|---|
| Apache Hive | SQL-on-Hadoop | • HiveQL (SQL-like) • Schema-on-read • Metadata store | Batch SQL queries on large datasets |
| Apache Impala | MPP SQL engine | • Low latency queries • HDFS/HBase integration • Native execution | Interactive SQL queries on Hadoop |
| Apache Druid | OLAP database | • Column-oriented storage • Real-time ingestion • Fast aggregations | Time series analytics, dashboards |
| Clickhouse | Column-oriented DBMS | • High performance • Real-time data updates • Linear scalability | Real-time analytics with high ingestion rates |
| Apache Kylin | OLAP engine | • MOLAP cube architecture • SQL interface • Sub-second query latency | Business intelligence on huge datasets |
| Dremio | Data lake engine | • Data reflections (acceleration) • Self-service semantics • Multi-source queries | Accelerating queries across data lake sources |
| SparkSQL | SQL engine for Spark | • DataFrame API • Catalyst optimizer • Native integration with Spark | SQL analytics within Spark applications |
Data Orchestration & Workflow Management
| Technology | Description | Key Features | Best Suited For |
|---|---|---|---|
| Apache Airflow | Workflow automation platform | • DAG-based workflows • Python for task definition • Extensible architecture | Complex ETL pipelines with dependencies |
| Apache NiFi | Data flow automation | • Visual web-based UI • Data provenance • Extensible processor model | Data routing, transformation, system mediation |
| Dagster | Data orchestrator | • Data-aware orchestration • Type-checked data handoffs • Testing framework | Data pipelines with complex interdependencies |
| Luigi | Pipeline framework | • Dependency resolution • Failure recovery • Command line integration | Batch jobs with complex dependencies |
| Prefect | Workflow management | • Hybrid execution model • Positive engineering • Dynamic workflows | Modern data stacks with complex flows |
| AWS Step Functions | Serverless orchestration | • Visual workflow editor • AWS service integration • Parallelization | Coordinating AWS services in serverless apps |
| Argo Workflows | Kubernetes-native workflows | • Container-native • CI/CD integration • Complex dependencies | Cloud-native data pipelines on Kubernetes |
Data Visualization & BI Tools
| Technology | Type | Strengths | Ideal For |
|---|---|---|---|
| Tableau | BI & visualization | • Intuitive interface • Rich visualizations • Strong data connectivity | Business users needing self-service analytics |
| Power BI | BI & visualization | • Microsoft ecosystem integration • Cost-effective • Regular updates | Organizations using Microsoft stack |
| Looker | Data platform | • LookML modeling layer • Git integration • Embedded analytics | Data-driven organizations needing governed self-service |
| Apache Superset | Open-source BI | • Interactive exploration • Wide visualization library • SQL IDE | Organizations wanting open-source modern BI |
| Grafana | Observability platform | • Time-series focused • Alerting capabilities • Plugin ecosystem | Real-time monitoring, operational dashboards |
| Kibana | Elasticsearch frontend | • Log & document exploration • Integration with Elastic Stack • Security features | Searching and visualizing log data |
| Redash | Query & visualization | • Multiple data source support • Query library • Shareable dashboards | SQL-based reporting and dashboards |
Machine Learning & AI for Big Data
| Technology | Category | Key Features | Use Cases |
|---|---|---|---|
| Spark MLlib | ML library | • Distributed algorithms • Pipeline API • Integration with Spark ecosystem | Large-scale ML integrated with data processing |
| TensorFlow on Spark | Distributed deep learning | • Distributed TensorFlow • CPU/GPU training • Model parallelism | Deep learning at scale on existing Spark clusters |
| H2O | Automated ML platform | • AutoML capabilities • R, Python, Scala APIs • GPU acceleration | Automated model building on large datasets |
| Ray | Distributed computing | • Task parallelism • Reinforcement learning • Distributed training | Scaling Python ML workflows and AI applications |
| MLflow | ML lifecycle platform | • Experiment tracking • Model registry • Model serving | End-to-end ML lifecycle management |
| Kubeflow | ML toolkit for Kubernetes | • End-to-end ML pipelines • Model training/serving • Notebook servers | ML workflows on Kubernetes |
| Databricks ML | Unified ML platform | • Feature store • AutoML • MLflow integration | End-to-end ML workflows with governance |
Big Data Integration & Ingestion Tools
| Technology | Type | Key Features | Best For |
|---|---|---|---|
| Apache Kafka Connect | Data integration for Kafka | • Source/sink connectors • Scalable architecture • Transformation capabilities | Streaming data integration with Kafka |
| Apache Sqoop | Database ingestion | • RDBMS to Hadoop transfers • Incremental imports • Parallel transfers | Batch loading data from relational databases |
| Apache Flume | Log collection | • Reliable data movement • Multiple sources/sinks • Extensible architecture | Collecting, aggregating, and moving log data |
| Talend | Data integration platform | • Visual interface • 1000+ connectors • Data quality features | Enterprise data integration with governance |
| Fivetran | Cloud ELT | • Automated schema management • Incremental updates • Monitoring & alerts | Turnkey data pipeline construction |
| Airbyte | Open-source ELT | • Growing connector library • Customizable connectors • Data synchronization | Building and maintaining ELT pipelines |
| Stitch | Cloud ETL service | • Simple interface • Quick setup • Comprehensive integrations | Quick setup for common data sources to destinations |
Container & Cluster Management
| Technology | Description | Key Features | Best For |
|---|---|---|---|
| Kubernetes | Container orchestration | • Auto-scaling • Service discovery • Declarative configuration | Managing containerized big data applications |
| Docker | Containerization platform | • Isolation • Reproducibility • Portability | Packaging data applications with dependencies |
| Apache Mesos | Cluster manager | • Two-level scheduling • Resource isolation • Scalability | Large-scale cluster resource management |
| YARN (Hadoop 3) | Resource manager | • Docker support • Federation • GPU isolation | Managing resources on Hadoop clusters |
| Amazon EKS | Managed Kubernetes | • AWS integration • Automated updates • Scalability | Running Kubernetes on AWS |
| Google Kubernetes Engine | Managed Kubernetes | • Autopilot mode • Auto-scaling • Multi-cluster management | Operating production-grade Kubernetes on GCP |
| Azure Kubernetes Service | Managed Kubernetes | • Integration with Azure services • DevOps integration • Security features | Running Kubernetes on Azure |
Key Big Data File Formats
| Format | Type | Characteristics | Best For |
|---|---|---|---|
| Parquet | Columnar | • Column-oriented • Efficient compression • Schema preservation | Analytical workloads, complex queries |
| ORC | Columnar | • Optimized for Hive • ACID support • Predicate pushdown | Hive-based analytical workloads |
| Avro | Row-based | • Schema evolution • Compact binary format • Rich data structures | Data serialization, schema evolution |
| JSON | Semi-structured | • Human-readable • Flexible schema • Wide support | APIs, configuration, flexible data |
| Protobuf | Binary serialization | • Compact binary format • Schema definition • Cross-language support | Efficient serialization with fixed schema |
| Delta | Table format | • ACID transactions • Schema enforcement • Time travel | Reliable data lake architecture |
| Iceberg | Table format | • Schema evolution • Hidden partitioning • Snapshot isolation | Large-scale analytics tables |
| Hudi | Table format | • Upserts & deletes • Incremental processing • Time travel | Record-level operations on data lakes |
Monitoring & Observability Tools
| Technology | Focus | Key Features | Best For |
|---|---|---|---|
| Prometheus | Metrics | • Time-series database • Powerful query language • Alert management | Monitoring Kubernetes and cloud-native applications |
| Grafana | Visualization | • Multi-source dashboards • Alerting • Annotation | Creating observability dashboards |
| Elastic APM | Application monitoring | • Distributed tracing • Performance metrics • Error tracking | End-to-end application monitoring |
| Datadog | Full-stack observability | • Infrastructure monitoring • APM • Log management | Complete visibility across distributed systems |
| New Relic | Full-stack observability | • Real-time metrics • Distributed tracing • Applied intelligence | Performance monitoring of applications and infrastructure |
| Splunk | Machine data platform | • Search & investigation • Alerts & dashboards • AI-powered analytics | Security, IT, and DevOps monitoring |
| Cloudera Manager | Hadoop management | • Cluster deployment • Configuration management • Service monitoring | Managing Hadoop ecosystem components |