redshift教程

目錄 (Table of Contents)

• Introduction

  介紹

• Setting up an Amazon Redshift Datasource

  設置Amazon Redshift數據源

• Querying Data From Your Datasource

  從數據源查詢數據

• Analyzing and Visualizing Your Data

  分析和可視化您的數據

• Adding Drilldowns to Your Visualizations

  在可視化中添加明細

• Querying Your Data Using Search-Based Analytics

  使用基于搜索的分析查詢數據

• Summary

  摘要

介紹 (Introduction)

Amazon Redshift is Amazon’s cloud-based relational database management system (RBDMS). Like most of Amazon’s offerings, Amazon Redshift is very popular, and for good reason: not only is it currently the fastest cloud data warehouse, but it gets faster every year.

Amazon Redshift是Amazon的基于云的關系數據庫管理系統(RBDMS)。 與Amazon的大多數產品一樣，Amazon Redshift也非常受歡迎，這有充分的理由：它不僅是目前最快的云數據倉庫，而且每年都在增長。

Here at Knowi, we offer broad native integration to Amazon Redshift for analytics and reporting. This enables our users to leverage the speed and scalability of Redshift without any constraints, and to quickly analyze data from Redshift and form valuable insights. If you’re interested in learning how to use Knowi to analyze data from Amazon Redshift, you’ve come to the right place.

在Knowi，我們為Amazon Redshift提供了廣泛的本機集成，以進行分析和報告 。 這使我們的用戶可以不受限制地利用Redshift的速度和可擴展性，并快速分析Redshift中的數據并形成有價值的見解。 如果您有興趣學習如何使用Knowi分析來自Amazon Redshift的數據，那么您來對地方了。

設置您的Amazon Redshift數據源 (Setting up Your Amazon Redshift Datasource)

After logging into your Knowi trial account, the first thing you’re going to do is connect to an Amazon Redshift Datasource and confirm that your connection is successful. This is how:

登錄到Knowi試用帳戶后 ，要做的第一件事是連接到Amazon Redshift數據源并確認連接成功。 這是這樣的：

1. Find “Data sources” on the panel on the left side of your screen and click on it.

1.在屏幕左側的面板上找到“數據源”，然后單擊它。

2. Head down to “Data Warehouses” and click on Amazon Redshift.

2.轉到“數據倉庫”，然后單擊Amazon Redshift。

3. We don’t need to change any of the parameters here; Knowi automatically enters all of them for us. Just click “Test Connection” at the bottom of your screen.

3.我們不需要在這里更改任何參數； Knowi會自動為我們輸入所有信息。 只需單擊屏幕底部的“測試連接”。

4. Once you’ve confirmed that your connection was successful, click “Save.”

4.確認連接成功后，單擊“保存”。

Your data source is now set up. Good work!

現在，您的數據源已建立。 干得好！

從數據源查詢數據 (Querying Data From Your Datasource)

Your datasource is now set up, which means it’s time to start querying your data. Here’s how to do this:

現在，您的數據源已建立，這意味著該開始查詢數據了。 這樣做的方法如下：

1. After saving your datasource, you should’ve received an alert at the top of your page that said “Datasource Added. Configure Queries.” Click on the word queries. (Otherwise, you can go back to the panel on the left side of your screen, go right below “Data Sources” and click on “Queries.” Then select “New Query +” from the top right.)

1.保存數據源后，您應該在頁面頂部收到一條警報，提示“已添加數據源。 配置查詢。” 單擊單詞查詢。 (否則，您可以返回屏幕左側的面板，轉到“數據源”下方，然后單擊“查詢”。然后從右上方選擇“新建查詢+”。)

2. Give your report a name inside “Report Name*” on the very top left of your screen. We’re going to analyze an email campaign here, so let’s call this one “Email Campaign.”

2.在屏幕左上方的“報告名稱*”中為報告命名。 我們將在這里分析電子郵件活動，因此我們將其稱為“電子郵件活動”。

3. In your Query Builder, click inside the “Tables” bar. Scroll down to “public.demo_sent” and click on that. This will automatically set up a Redshift query that returns the data within this table.

3.在查詢生成器中，單擊“表”欄內。 向下滾動到“ public.demo_sent”，然后單擊。 這將自動設置Redshift查詢，該查詢返回此表中的數據。

4. Head over to the bottom left hand of your screen and click on the blue “Preview” button in order to preview the data. You should see the results of an email campaign that includes various data such as the number of emails sent, opened, and clicked on, as well as the message type and the customer.

4.轉到屏幕的左下角，然后單擊藍色的“預覽”按鈕以預覽數據。 您應該看到一個電子郵件活動的結果，其中包含各種數據，例如發送，打開和單擊的電子郵件數，以及消息類型和客戶。

5. Once you’ve looked over your data, scroll to the bottom right corner of your screen and click the green “Save & Run Now” button.

5.查看完數據后，滾動到屏幕的右下角，然后單擊綠色的“立即保存并立即運行”按鈕。

As soon as your query was successfully completed, Knowi automatically saved the results of your query as a virtual dataset and then stored the results of that query as a dataset within its elastic data warehouse. Knowi does this every time you run a query.

成功完成查詢后，Knowi會自動將查詢結果保存為虛擬數據集，然后將該查詢結果存儲為彈性數據倉庫中的數據集。 每當您運行查詢時，Knowi都會這樣做。

分析和可視化您的數據 (Analyzing and Visualizing Your Data)

Although you spent a little bit of time looking over your data, it’s unlikely that you learned anything from it in the format that it was in. There are a ton of things that we could figure out with our data, but let’s say we had to answer a burning question: do emails sent to certain customers have a higher conversion rate? Knowi allows us to efficiently answer this question and then visualize our results with the following steps:

盡管您花了一些時間查看數據，但您不太可能以其所使用的格式從數據中學到任何東西。我們可以從數據中找出很多東西，但是我們不得不回答一個緊迫的問題：發送給某些客戶的電子郵件的轉換率更高嗎？ Knowi使我們能夠有效地回答這個問題，然后通過以下步驟可視化我們的結果：

1. Head to the top of the panel on the left side of your screen and click on “Dashboards.” Click the orange plus icon and name your dashboard. We’ll call this one “Email Visualization.”

1.轉到屏幕左側面板的頂部，然后單擊“儀表板”。 單擊橙色加號圖標，然后為您的儀表板命名。 我們將其稱為“電子郵件可視化”。

2. Head back to the panel, just below “Dashboards,” and click on “Widgets.” Select the “Email Campaign” widget that you just created and drag it onto your dashboard.

2.回到“儀表板”下方的面板，然后單擊“窗口小部件”。 選擇您剛剛創建的“電子郵件活動”小部件，然后將其拖動到儀表板上。

3. Right now, the visualization that you see is just a data grid. We’re going to change this to something a little easier on the eyes, but first we have to add the metric that we’re looking for. In order to do this, scroll to the top right corner of your widget. Click on the 3 dot icon, then scroll down to “Analyze” and click on it.

3.現在，您看到的可視化只是一個數據網格。 我們將改變它，使之看起來更輕松一些，但是首先我們必須添加我們要尋找的指標。 為此，請滾動至小部件的右上角。 單擊3點圖標，然后向下滾動到“分析”并單擊它。

4. Head to the top left corner of your screen and find “+Add Function.” The function that we’re looking to create is very simple: it’s called “conversion rate” and it’s calculated by dividing “conversions” by “sent.” In order to calculate this, click on “+Add Function,” then set “Name” as Conversion Rate and “Operation” as (conversions/sent)*100.

4.轉到屏幕的左上角，然后找到“ +添加功能”。 我們希望創建的功能非常簡單：稱為“轉化率”，通過將“轉化”除以“已發送”來計算。 為了計算該值，請單擊“ +添加功能”，然后將“名稱”設置為“轉換率”，將“操作”設置為(轉換/發送)* 100。

5. Right now, all we see is the conversion rate of each individual email campaign. What we want to see is the conversion rate of all email campaigns grouped by customer, and we also want our data sorted by conversion rate. First, we need to drag the “customer” bar from the left side of the screen over to the “Grouping/Dimensions:” box and let go.

5.現在，我們看到的只是每個電子郵件廣告系列的轉化率。 我們要查看的是按客戶分組的所有電子郵件廣告系列的轉化率，我們還希望我們的數據按轉化率排序。 首先，我們需要將“客戶”欄從屏幕左側拖到“分組/維度：”框中，然后放開。

6. Now we just need to drag our new “Conversion Rate” metric from “Fields/Metrics:” over to “Sort by:” and change the direction to descending in order to sort our data from the highest conversion rate to the lowest. As you can see, Facebook emails have a conversion rate of just over 1%, while Netflix emails have a conversion rate of less than 0.5%.

6.現在，我們只需要將新的“轉化率”指標從“字段/指標：”拖到“排序依據：”，然后將方向更改為下降，以便將數據從最高轉化率降到最低。 如您所見，Facebook電子郵件的轉換率剛剛超過1％，而Netflix電子郵件的轉換率不到0.5％。

7. Now it’s time to visualize everything. Head back to the top of your screen and click on “Visualization.” Change your visualization type from “Data Grid” to “Column.”

7.現在是時候可視化所有內容了。 回到屏幕頂部，然后單擊“可視化”。 將可視化類型從“數據網格”更改為“列”。

8. Now you can see each customer ranked by the conversion rate of their emails. Head to the top right corner of your screen and click on the “Clone” icon which looks like two pieces of paper. Name this one “Email Campaign — Conversion Rates” and click the orange “Add to Dashboard” button.

8.現在，您可以看到按客戶的電子郵件轉換率排名的每個客戶。 轉到屏幕的右上角，然后單擊看起來像兩張紙的“克隆”圖標。 將此命名為“電子郵件廣告系列-轉化率”，然后單擊橙色的“添加到儀表板”按鈕。

Just like that, you’ve turned your raw data into a visualization that contains valuable information. The knowledge that Facebook’s conversion rate is about two and a half times higher than Netflix’s may be factored into future decision making.

就像這樣，您已將原始數據轉換為包含有價值信息的可視化文件。 Facebook的轉換率大約是Netflix的兩倍半，這可能是將來的決策依據。

在可視化中添加明細 (Adding Drilldowns to Your Visualization)

The next step to improving our visualization is to make it more interactive and navigable by adding drilldowns. Drilldowns are a powerful feature within Knowi that allow the user to take a deeper dive into a filtered section of the raw data with just one click. Here’s how we’ll add a drilldown to our widget:

改善可視化效果的下一步是通過添加向下鉆取使其更具交互性和導航性。 向下鉆取是Knowi中的一項強大功能，使用戶只需單擊一下即可深入了解原始數據的過濾部分。 這是我們向小部件添加明細的方法：

1. Click on the 3 dot icon in the top right corner of your new widget, scroll down to “Drilldowns” and click on it.

1.單擊新窗口小部件右上角的3點圖標，向下滾動到“向下鉆取”，然后單擊它。

2. Set your Drilldown type as “Widget,” set it to drill into “Email Campaign” when “Customer” is clicked, and set customer = customer in your optional drilldown filters. Click the orange “Save” button at the bottom right corner of the Drilldowns popup.

2.將“向下鉆取”類型設置為“窗口小部件”，將其設置為在單擊“客戶”時鉆入“電子郵件活動”，然后在可選的向下鉆取過濾器中設置“客戶=客戶”。 單擊“向下鉆取”彈出窗口右下角的橙色“保存”按鈕。

3. Test it out by clicking on Facebook, the customer whose conversion rate is the highest. As you can see, this returns every campaign where Facebook was the customer. Then get back to your original visualization, head back to the top right corner of your widget and click on the left arrow icon in the middle of that corner.

3.通過單擊Facebook(轉化率最高的客戶)進行測試。 如您所見，這將返回以Facebook為客戶的每個廣告系列。 然后回到原始的可視化效果，回到小部件的右上角，然后單擊該角中間的向左箭頭圖標。

使用基于搜索的分析查詢數據 (Querying Your Data with Search-Based Analytics)

Your dashboard is set up, which means you’re fully prepared to query your data using search-based analytics. This means you’re ready to share your dashboard and your data with anybody who speaks English, even if they’re unfamiliar with Knowi. Here’s how to query your data using search-based analytics:

儀表盤已設置完畢，這意味著您已準備好使用基于搜索的分析來查詢數據。 這意味著您已經準備好與任何會說英語的人共享儀表板和數據，即使他們不熟悉Knowi。 以下是使用基于搜索的分析查詢數據的方法：

1. Head to the top right corner of your original “Email Campaign” widget and click on the 3 dot icon. Scroll down and click on “Analyze.”

1.轉到原始“電子郵件活動”窗口小部件的右上角，然后單擊3點圖標。 向下滾動并單擊“分析”。

2. Let’s say you want to monitor email activity by month in order to see if things looked any different in different months. In order to do this, head to the search bar at the top of your screen and type “total sent, total opened, total clicks, total conversions by month” and then hit enter. Knowi’s natural language processing will quickly provide you with what you’re looking for.

2.假設您要按月監視電子郵件活動，以查看不同月份的情況是否有所不同。 為此，請轉到屏幕頂部的搜索欄，然后輸入“發送總數，打開總次數，點擊總數，每月總轉化次數”，然后按Enter。 Knowi的自然語言處理將Swift為您提供所需的內容。

3. Now it’s time to visualize this data. Head back to “Visualization” and set the visualization type to “Area.” This will show us the total number of emails sent, and the total number of conversions per month. The conversions are so low that it’s hard to see any movement in the number with our naked eye, but that’s okay.

3.現在是時候可視化這些數據了。 返回“可視化”并將可視化類型設置為“區域”。 這將向我們顯示已發送的電子郵件總數以及每月的轉換總數。 轉換率如此之低，以至于我們肉眼很難看到數字的變化，但這沒關系。

4. Head back to the top right and click on the “clone” icon once again. Name this widget “Sent and Conversions — Area,” clone it, and then add it to your dashboard.

4.回到右上角，然后再次單擊“克隆”圖標。 將此小部件命名為“發送和轉換-區域”，將其克隆，然后將其添加到您的信息中心。

5. Last, head back to your dashboard. Drag your new “Email Campaign — Area Visualization” widget to the top of your dashboard, which will bring the original “Email Campaign” widget to the bottom.

5.最后，回到儀表板。 將新的“電子郵件活動-區域可視化”小部件拖動到儀表板的頂部，這會將原始的“電子郵件活動”小部件帶到底部。

This data conveys another valuable piece of insight: these email campaigns receive a low number of opens and clicks and an extremely low number of conversions for every email that they send. While these numbers remain consistently low every month, they do seem to increase alongside the number of total emails sent.

這些數據傳達了另一個有價值的見解：這些電子郵件廣告系列收到的打開和點擊次數很少，而發送的每封電子郵件的轉化次數也很少。 盡管這些數字每個月始終保持較低水平，但似乎隨著發送的電子郵件總數的增加而增加。

It’s also important to remember that we didn’t need extensive coding knowledge or experience with Knowi to do what we just did. The low barriers to usage here makes Knowi’s dashboards accessible to any curious English speaker.

同樣重要的是要記住，我們不需要廣泛的編碼知識或豐富的Knowi經驗即可完成我們剛剛做的事情。 此處使用的障礙很低，因此任何好奇的英語使用者都可以使用Knowi的儀表板。

摘要 (Summary)

In summary, we connected to an Amazon Redshift Datasource and made a query on our new datasource. This stored the results of our query in Knowi’s elastic data warehouse. We then analyzed and visualized our data, and added drilldowns to our visualization that allow the user to drill in on a filtered section of the raw data that they’d like to learn more about. Lastly, we used search-based analytics to answer another question and visualize our answer.

總而言之，我們連接到Amazon Redshift數據源，并對新數據源進行了查詢。 這將查詢結果存儲在Knowi的彈性數據倉庫中。 然后，我們對數據進行了分析和可視化，并在可視化中添加了向下鉆取，從而使用戶可以深入了解他們想了解更多信息的原始數據的過濾部分。 最后，我們使用了基于搜索的分析來回答另一個問題并可視化我們的答案。

翻譯自: https://towardsdatascience.com/analyzing-visualizing-amazon-redshift-data-tutorial-239aa6443d43

redshift教程

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