havermeyer

Late reply, but which things is it missing for your needs? I work on the BigQuery team, so I can pass along any feedback that you have.

One of the reasons that I like Twitter... It has a short section with "important" (by some metric) tweets since the last time that you viewed your feed, followed by tweets in chronological order. Facebook surely has the ability to do something similar but it would negatively impact how much time people spend on the site.

Politicians play by a different set of rules: https://blog.twitter.com/official/en_us/topics/company/2018/.... From the blog:

> Blocking a world leader from Twitter or removing their controversial Tweets would hide important information people should be able to see and debate. It would also not silence that leader, but it would certainly hamper necessary discussion around their words and actions.

From today's release notes (https://cloud.google.com/bigquery/docs/release-notes#july_25...):

> BigQuery has achieved approximately 5x improvement in TPC-DS 10T power run geomean over the past 18 months.

Outside of that, BigQuery now provides better partitioned table functionality (https://cloud.google.com/bigquery/docs/partitioned-tables) as well as clustering (https://cloud.google.com/bigquery/docs/clustered-tables), both of which can help to reduce costs and improve performance. In terms of pricing, many large customers have reserved slots (https://cloud.google.com/bigquery/docs/slots), where you pay a fixed monthly cost in exchange for dedicated resources, similar to running a large Redshift instance.

As a disclosure, I work on the BigQuery team.

Documentation, for anyone wanting to get started: https://cloud.google.com/bigquery/docs/bigqueryml-intro.

(Disclosure: I'm on the BigQuery team)

Android too :) It holds up really well considering its age.

https://play.google.com/store/apps/details?id=com.atari.mobi...

This is how CTEs work in BigQuery (using standard SQL), for example. There are advantages and disadvantages both ways, but the benefit of making CTEs behave like subqueries is that the query engine can push down filters into table scans.

It should work fine either way. Using standard SQL in BigQuery, though, you can do:

    #standardSQL
    SELECT pop, repo_name, path
    FROM (
      SELECT id, repo_name, path
      FROM `bigquery-public-data.github_repos.files` AS files
      WHERE path LIKE '%pom.xml' AND
        EXISTS (
          SELECT 1
          FROM `bigquery-public-data.github_repos.contents`
          WHERE NOT binary AND
            content LIKE '%commons-collections<%' AND
            content LIKE '%>3.2.1<%' AND
            id = files.id
        )
    )
    JOIN (
      SELECT
        difference.new_sha1 AS id,
        ARRAY_LENGTH(repo_name) AS pop
      FROM `bigquery-public-data.github_repos.commits`
      CROSS JOIN UNNEST(difference) AS difference
    )
    USING (id)
    ORDER BY pop DESC;

Better yet, it runs faster than the legacy SQL query :)

As a disclosure, I work on the project to support standard SQL in BigQuery.

It has the same basis as standard SQL in BigQuery: https://cloud.google.com/bigquery/docs/reference/standard-sq...