from data lakes and Kafka topics to RAG, agentic AI, and context engineering. Each entry links to an in-depth explainer. keywords: data glossary,big data glossary,ai glossary,data engineering terms,search glossary,what is glossary,bigdataboutique knowledge base subtitle: cta: true
This page is a working reference of the technologies, concepts, and roles we operate across the modern data, search, and AI stack. Every entry links to a longer explainer covering how the thing works, where it fits, what to watch out for, and how it relates to the rest of the ecosystem. The goal isn't encyclopedic completeness -- it's giving practitioners a concise, opinionated grounding in the parts of the stack we actually build with in production.
If you're new to one of these areas, the entries are designed to be read in any order. If you're sizing up an architecture decision, the cross-links at the end of each page point to the adjacent topics you'll need to weigh.
Data Lakes, Warehouses, and Lakehouses
Where structured and unstructured data actually lives in modern analytical platforms -- and how the three architectural patterns differ in cost, performance, and operational profile.
- What is a Data Lake? -- raw, schema-on-read storage on object storage like S3, the foundation under most modern analytics.
- What is a Data Warehouse? -- structured, modeled, query-optimized stores for BI and SQL analytics.
- What is a Data Lakehouse? -- combining lake economics with warehouse reliability through open table formats.
- What is Apache Iceberg? -- the dominant open table format for lakehouse architectures.
- What is Snowflake? -- cloud-native data platform with separated storage and compute.
- ETL vs ELT -- where transformation happens and why the order matters.
Data Pipelines, Streaming, and Ingestion
The systems that move data from where it's produced to where it's consumed, in batch or in real time.
- What is a Data Pipeline? -- automated movement and transformation of data across systems.
- What is a Kafka Topic? -- the partitioned, durable log that powers event streaming.
- What is Apache Flink? -- distributed stream processing with strong exactly-once guarantees.
- What is Airbyte? -- open-source data integration for moving data into the warehouse or lake.
- What is Fivetran? -- managed ELT for SaaS and database sources.
- What is dbt? -- SQL-based transformation framework that has become the standard for ELT workflows.
- What is ClickPipes? -- managed ingestion for ClickHouse Cloud.
- What is DataOps? -- applying DevOps practices to data pipelines and analytics.
Search and Real-Time Analytics
Engines built for full-text search, log analytics, and sub-second analytical queries -- workloads a general-purpose warehouse handles poorly.
- What is Elasticsearch? -- the most widely deployed open-source search and analytics engine.
- What is OpenSearch? -- the open-source fork of Elasticsearch maintained under the OpenSearch Software Foundation.
- What is AWS Elasticsearch? -- the history and current state of Amazon's Elasticsearch and OpenSearch managed services.
- What is Apache Solr? -- the Lucene-based search engine that predates Elasticsearch.
- What is the ELK Stack? -- Elasticsearch, Logstash, and Kibana for log analytics.
- What is ClickHouse? -- the columnar database for sub-second analytics at huge scale.
Cloud Computing and Managed Databases
The infrastructure layer that the data stack now sits on, and the managed database services that ride on top of it.
- What is Cloud Computing? -- service models, deployment models, and how the cloud reshaped data architectures.
- What is AWS RDS? -- AWS's managed relational database service.
- What is Amazon Aurora? -- AWS's cloud-native relational database with distributed storage.
Generative AI: Models, Agents, and RAG
The systems and patterns behind production GenAI applications -- foundation models, retrieval, agents, observability.
- What is Amazon Bedrock? -- AWS's managed service for foundation models, knowledge bases, agents, and guardrails.
- What is RAG? -- Retrieval-Augmented Generation: grounding LLM responses in retrieved context.
- What is GraphRAG? -- adding a knowledge graph traversal layer to RAG for multi-hop reasoning.
- What is Agentic AI? -- AI systems that plan, use tools, and act autonomously toward goals.
- What is a Vector Database? -- the storage layer behind semantic search and RAG.
- What is an MCP Server? -- the Model Context Protocol and how it standardizes tool access for AI agents.
- What is the A2A Protocol? -- the Agent-to-Agent protocol for multi-agent communication.
Prompting, Context, and LLM Application Engineering
The disciplines that distinguish production LLM applications from prototypes -- prompting, context management, orchestration, observability.
- What is Prompt Engineering? -- designing and refining LLM inputs to produce reliable outputs.
- What is Context Engineering? -- the broader discipline of designing everything in the model's context window.
- What is LangChain? -- the most widely used LLM application framework.
- What is LangGraph? -- graph-based orchestration for stateful agents and multi-agent workflows.
- What is LangSmith? -- LangChain's hosted observability and evaluation platform.
- What is Langfuse? -- open-source LLM observability and prompt management.
Observability and Operations
The monitoring, logging, and operational tooling that keeps production data and AI platforms reliable.
- What is OpenTelemetry? -- the vendor-neutral standard for traces, metrics, and logs.
- What is Datadog? -- the dominant SaaS observability platform.
- What is Grafana? -- open-source dashboards and visualization, the de facto front end for many observability stacks.
- What is Openclaw? -- the OpenSearch-native AI assistant we built for cluster operations.
Roles and Practices
How the work itself is changing as data and AI become more central to engineering teams.
- What is a Forward Deployed Engineer? -- the embedded-engineer model that has become the norm for high-touch AI and data work.
Working With Us
If you're navigating any of these decisions in production -- choosing between a warehouse and a lakehouse, designing a Kafka topology, picking a vector database, building a RAG system on Bedrock, migrating from Elasticsearch to OpenSearch -- we operate all of the above at scale across Fortune 100 enterprises and high-growth startups. See our services page or get in touch to discuss your architecture.