Blog
Tutorials, guides, and deep dives on data engineering, AI agents, and the DataSpoc platform.
Data Mesh Without the Complexity: One Bucket Per Team
Implement data mesh with DataSpoc. Each team gets their own bucket, their own pipelines, their own AI agent. No central bottleneck. No platform team required.
Building a Medallion Architecture Data Lake with DataSpoc
Bronze, Silver, Gold — implement the Databricks medallion pattern using DataSpoc Pipe and Lens. No Spark, no cluster, no cost.
The AGENT.md Pattern: Teaching AI Agents to Use Your Tools
How a single Markdown file turns any AI agent into a power user of your data platform.
The $0 Data Lake for Startups: DataSpoc + S3 in 30 Minutes
A complete data platform for early-stage startups. Ingest from Postgres and Stripe, query with AI, connect agents.
10 Lines of YAML vs 200 Lines of Airflow DAG
Side-by-side comparison of building the same ETL pipeline with Airflow and DataSpoc Pipe.
One Data Lake, Three Clouds: Multi-Cloud Analytics with DataSpoc
Register S3, GCS, and Azure buckets in DataSpoc Lens and query across all of them with one SQL statement.
Query Your Data Lake with AI for Free: Ollama + DataSpoc Lens
No API key needed. Run AI queries on your data lake using a local LLM with Ollama.
Data Analysis from Windsurf IDE with DataSpoc MCP
Connect Windsurf to your data lake. Query, analyze, and build reports without leaving your editor.
Jupyter Notebook on Your Data Lake in 60 Seconds
Launch JupyterLab with all your cloud Parquet tables pre-mounted. Zero configuration.
Incremental Extraction Patterns: Never Re-Extract Old Data Again
How DataSpoc Pipe uses Singer bookmarks to only fetch new data. Save time, bandwidth, and cloud costs.
Building a Curated Data Layer with SQL Transforms (No dbt Required)
Numbered SQL files in DataSpoc Lens replace dbt for simple data transformation workflows.
From Google Sheets to a Queryable Data Lake in 3 Commands
Turn any public Google Sheets spreadsheet into Parquet files you can query with SQL.
Reactive Data Analysis with Marimo and DataSpoc Lens
Marimo notebooks + DuckDB on your data lake. Cells update automatically when you change a query.
MongoDB to Parquet: Build a Data Lake from Your MongoDB in 15 Minutes
Use DataSpoc Pipe with tap-mongodb to extract collections and convert them to queryable Parquet files.
Building a Data Query Agent with Anthropic Claude SDK and DataSpoc
Use Claude's tool_use feature to let the model query your data lake with real SQL.
Building a Multi-Agent Data Team with CrewAI and DataSpoc
A DE agent ingests data, a DA agent analyzes it, an ML agent trains models — all powered by DataSpoc.
LlamaIndex + DataSpoc: Query Your Data Lake Without Embeddings
Use LlamaIndex's tool-calling agents with DataSpoc Lens SDK for accurate, grounded data queries.
DuckDB vs Spark for Data Lake Queries: When Each Wins
DuckDB powers DataSpoc Lens. Spark powers Databricks. Here's when to use each — with benchmarks.
How to Build an MCP Server for Your Data Lake
Turn your cloud Parquet files into an API for AI agents with DataSpoc Lens MCP.
Analyze Your Data Lake from Cursor IDE with DataSpoc MCP
Connect Cursor to your data lake via MCP. Query tables, explore schemas, and build reports without leaving your editor.
We Replaced Our $8k/month Snowflake with DuckDB and Parquet
How DataSpoc Lens gives you a virtual warehouse over cloud Parquet — at zero cost.
Migrating from Fivetran to DataSpoc Pipe: A Step-by-Step Guide
Save $2k/month by replacing Fivetran with open-source DataSpoc Pipe. Same sources, same destinations, zero cost.
RAG vs SQL: Why Your AI Agent Should Query, Not Embed
RAG hallucinates on structured data. Here's why MCP + SQL is more accurate, faster, and cheaper.
Data Governance for AI Agents: How DataSpoc Keeps Your Lake Secure
Read-only access, cloud IAM enforcement, and audit trails — how to let AI agents query without risk.
Building a Data Analyst Agent with LangGraph and DataSpoc Lens
A LangGraph agent that discovers tables, writes SQL, and answers business questions using DataSpoc Lens SDK.
Postgres to S3 in 5 Minutes with DataSpoc Pipe
Replace your Airflow DAGs with one CLI command. Incremental extraction from PostgreSQL to Parquet in S3.
Ingest Any REST API to Parquet in 10 Minutes
Use DataSpoc Pipe with tap-rest-api to pull data from any API endpoint into your data lake.
Multi-Agent Analytics with AutoGen and DataSpoc
Build an AutoGen team where agents collaborate to analyze your data lake — one queries, one visualizes, one reports.
Using Claude Code as Your Data Engineer: MCP + DataSpoc
Configure Claude Code to ingest data, query your lake, and debug pipelines — all from the terminal.
RAG on Parquet: How to Build Retrieval Over Your Data Lake Without Embeddings
Skip the vector store. Use DataSpoc Lens to give your LLM direct SQL access to Parquet files.
LangChain SQLDatabaseChain vs DataSpoc Lens: Which Is Better for Data Queries?
Side-by-side comparison of querying databases with LangChain's SQL chain versus DataSpoc Lens SDK and MCP.
Building a Data Lake Agent with OpenAI Function Calling and DataSpoc
Use OpenAI's function calling to let GPT-4 query your data lake with real SQL via DataSpoc Lens.