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JetBrains for Data
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@JetBrainsData

JetBrains for Data

JetBrains
@JetBrainsData
Feel the difference in every data task with PyCharm, DataGrip, DataSpell, and Datalore. Intelligent tools for your entire data workflow, by @JetBrains
jetbrains4data.com
Joined August 2025
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  • @JetBrainsData
    JetBrains for Data
    JetBrains
    @JetBrainsData
    Apr 28
    Every data team exploring agentic analytics hits the same question: Should we build this ourselves or use a platform? At first, building looks simple. You connect an LLM to your data, add some docs, and it works.
    1
  • @JetBrainsData
    JetBrains for Data
    JetBrains
    @JetBrainsData
    Apr 14
    There are several ways to connect an AI agent to your data. At first glance, all three approaches give you the same thing : answers in natural language. But the real question is: Can you trust those answers?
    1
  • @JetBrainsData
    JetBrains for Data
    JetBrains
    @JetBrainsData
    Mar 23
    The data analyst role is changing. In the dashboard era, analysts wrote queries and built charts. In the agent era, the job becomes defining metrics, building semantic contracts and designing guardrails for AI systems. 🧵
    1
  • @JetBrainsData
    JetBrains for Data
    JetBrains
    @JetBrainsData
    Mar 16
    Two analysts can answer the same question and get two different answers. Now imagine AI agents doing the same thing. That’s the agent swarm problem in a nutshell. Agentic analytics doesn’t need smarter agents – it needs a shared semantic foundation.
    1
  • @JetBrainsData
    JetBrains for Data
    JetBrains
    @JetBrainsData
    Mar 13
    When building AI agents, people tend to run into the same issues: 🫠 It kind of works, but I don’t know why. 🫠 The agent is hard to extend. 🫠 It’s hard to debug. 🫠 It’s hard to trust. Check a free step-by-step course by @t_redactyl on building AI agents with Python,