Multiple commenters recommend ClickHouse as a high-performance alternative to OpenSearch or Elasticsearch for querying a 7TB structured dataset, highlighting its full-text search indices and...

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    "domain": "clickhouse.com"
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    "summary": "Multiple commenters recommend ClickHouse as a high-performance alternative to OpenSearch or Elasticsearch for querying a 7TB structured dataset, highlighting its full-text search indices and superior performance on joins.",
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    "comments": [
      {
        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbj4uzm/",
        "depth": 0,
        "score": 37,
        "author": "BlackHole_Rider",
        "excerpt": "use icerberg open table format.",
        "posted_at": "2026-09-23T09:08:02.000Z",
        "author_url": "https://www.reddit.com/user/BlackHole_Rider/"
      },
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        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbji9b3/",
        "depth": 0,
        "score": 14,
        "author": "Slampamper",
        "excerpt": "usually the tool is not the answer, first think if you can better partition the data, is it possible to create it in such a way that the query engine can easily filter on results and thus query less?\nAre all results always necessary or can you further archive old data, can you filter on id ranges, etc etc.",
        "posted_at": "2026-09-23T10:54:42.000Z",
        "author_url": "https://www.reddit.com/user/Slampamper/"
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      {
        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbj8vc6/",
        "depth": 0,
        "score": 14,
        "author": "fdqntn",
        "excerpt": "It's pointless to propose solutions if you dont give access pattern and SLA (ie it should come back in below 10ms).\n\n ElasticSearch is a particularly bad advice if you just need to query by an id, this would be crazy wastefull.",
        "posted_at": "2026-09-23T09:42:34.000Z",
        "author_url": "https://www.reddit.com/user/fdqntn/"
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        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbjq3z4/",
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        "author": "notmarc1",
        "excerpt": "Partition your data based on the query patterns",
        "posted_at": "2026-09-23T11:45:43.000Z",
        "author_url": "https://www.reddit.com/user/notmarc1/"
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        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbiyi01/",
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        "author": "MarchewkowyBog",
        "excerpt": "You might want to look into clickhouse. You didn't really explain what the queries or data is. But because you point to parquet on s3 and athena I presume it's more structured then unsteuctured. Clickhouse has full-text search indecies. Depending on the query it might perform better then elastic or opensearch. If any joins are involved it will most certainly perform better",
        "posted_at": "2026-09-23T08:11:23.000Z",
        "author_url": "https://www.reddit.com/user/MarchewkowyBog/"
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        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbjtv3m/",
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        "score": 5,
        "author": "ludflu",
        "excerpt": "you should probably think about access patterns and typical queries before jumping straight to the tech. What are the kinds of questions do people want to answer using this data?\n\n Use those questions to help understand how you should structure the data, then use that structure to help decide what tech would be a good fit.",
        "posted_at": "2026-09-23T12:07:41.000Z",
        "author_url": "https://www.reddit.com/user/ludflu/"
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        "author": "JJGreenerTinejo",
        "excerpt": "Clickhouse",
        "posted_at": "2026-09-23T08:09:52.000Z",
        "author_url": "https://www.reddit.com/user/JJGreenerTinejo/"
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        "url": "https://www.reddit.com/r/dataengineering/comments/1wnmevk/comment/pbjgqfj/",
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        "author": "Difficult-Bag1550",
        "excerpt": "if users query by id, you could add bucketing to this column",
        "posted_at": "2026-09-23T10:44:01.000Z",
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      "[comment u/JJGreenerTinejo] Clickhouse"
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    "post_text": "Hi all, hoping to get some advice here as I’ve never dealt with setting up storage and query facility for a dataset as large as this before.\n\nI have a large dataset of parquet files, partitioned by date, sat in an S3 bucket. Currently users are utilising Athena to query the dataset via Glue, however as they tend to be querying via an id or via a text search, the results can sometimes take more than 10 minutes just to return one row.\n\nI’ve looked into using OpenSearch, which seems to match what I’d need (users will primarily want to do full-text search, the date partitioning is irrelevant) but I’m concerned about the cost, as AI generated estimates are telling me at the very least it would be around 65k a month.\n\nAre there any better approaches to this or any routes I should consider? Or is this simply the cost of querying big data?",
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curl https://signals.autobound.ai/v1/signals/28580f4d-9db1-5575-a6e3-6aaf93ebd35c \
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curl -X POST https://signals.autobound.ai/v1/companies/enrich \
  -H "X-API-KEY: $AUTOBOUND_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain":"clickhouse.com","limit":20}'