databricks.
SQL演示

我總是喜歡做的第一件事就是探索我可用的數據。因此，您將注意到這裏在數據資源管理器中，我有一個點，然後單擊了解屬於我的數據湖的不同目錄，數據庫和表。在這裏，如果我想快速查看不同的表，我可以概述模式，我可以看幾行樣本數據。現在當然，所有這些都以打開的格式存儲，我可以看到詳細信息，我甚至可以使用單擊按鈕管理權限。現在，您在Databricks SQL中擁有的所有用戶和組都可以從OKTA，Active Directory或其他身份驗證提供程序中同時同步。什麼是尼斯在這個數據資源管理器中，我也可以看到我的delta表的曆史，所以我可以了解發生的每一個操作。現在，我們將使用我們為您提供的紐約州的紐約州納米出租車數據集。但它也值得注意的是，如果你想從另一個來源設置攝取，你可以用一個像fivetran這樣的合作夥伴，隻點擊幾下。好吧，讓我們開始查詢。所以在這裏，你會發現我們有什麼是你的SQL工作台。 And this is where you author and execute queries. So let’s go ahead and run this sample one. Now, of course, you can have multiple SQL statements, whether those are ad hoc queries, or DDLs in each of these tabs. I’m also going to go ahead and pin this table since we’re going to use it throughout, while I can kind of go ahead through the schema browser and take a look at some of the other data that I have available to me.

現在，有一件事要注意在這裏，我可以在這裏使用每個工作空間出來的框架中的默認SQL端點。但是，這些端點的偉大是他們消除了集群的複雜性，您不必考慮版本，您不必考慮Spark配置。你剛挑選T恤尺寸，小，中，大。您可以將它們分配給特定的團隊或部門。因此，您可以簡化政府治理和退款，並很快使用新的無法無關端點，您無需在短短幾秒鍾內準備好的啟動時間。

所以現在我們有了我們的結果，為什麼我們不看一些執行細節？你在這裏看到我們對實際發生的事情看起來很好看。我們還可以了解我們運行的一些執行情況，如果我們願意，請返回以前的版本。If we’re really firing on all cylinders, we can also go ahead and leverage any of these keyboard shortcuts, for things like opening and closing new tabs, or even executing our queries, highlighting certain portions, everything designed to make your analysis as quick and easy as possible. Now, of course, sometimes you don’t just want to generate results, you also want to visualize them and share them with other individuals and teams in the organization. So why don’t we go ahead and add a visualization. And you’ll see that’s right side by side with my with my results, let’s just do a quick scatterplot of the average fare by route, see if we can figure out which which routes we probably want to avoid, because they’re a little pricey. So you can see here, I was able to quickly just add that, that visualization. And I can quickly spot any of these outliers that I might want to avoid when I’m planning my trips – probably better to take the subway. So now this is great, I’m starting to generate some insights already. But what if I want to organize these insights per system and kind of come back to them time and time again with my team? Dashboards are a great way to do this. And of course, I’m sure you’ve guessed by now, we provide a number of sample dashboards out of the box. So let’s go ahead and pick our NYC Taxi one.

Now, you’ll see that this dashboard provides a great example of a number of capabilities you might want to take advantage of, whether that’s conditionally coloring certain values in a table, or adding interactivity via parameters and allowing the consumers of these dashboards to set different date ranges. And what’s great is if there’s a key metric I’m tracking like total trips, I can also right within Databricks SQL set up an alert so I know when certain thresholds are met. So here you’ll notice that I’ve selected my query total trips, I’ve decided I want to know as soon as we crossed that magical 300 mark for that time range. And we just want to get notified once when it happens. So I’m going to go ahead and create the alert. I’m going to tell it to send to myself. And I’m also going to go ahead and decide to make sure the entire team knows too, and make sure that message goes to Slack as well.

好的，所以，我們為什麼不跳到Tableau，我已經已經將它配置為直接從Databricks SQL進行了查詢並達到那些SQL端點。您可以在此處看到我能夠在Tableau儀表板中開發與我的數據的相同視圖。如果我想打開任何東西並做一些額外的分析，就像讓我們說我想通過下拉代碼下降方式了解路線票價的差異。您可以很快看到這裏，我可以看到哪些郵政編碼彼此非常靠近。您可以看到非常短的旅行，非常便宜的旅行。但如果我進一步走了一點，我也可以開始獲得更多的見解，了解距離實際上的距離稍長。這一切都是針對我的DataBricks SQL數據生成實時查詢，單擊按鈕。所以希望，這讓您了解數據湖上的分析是否有可能使用我們的內置功能或最喜歡的BI工具。