Simplify knowledge evaluation and collaboration with SQL Notebooks in Amazon Redshift Question Editor V2.0







Amazon Redshift Question Editor V2.0 is a web-based analyst workbench that you need to use to writer and run queries in your Amazon Redshift knowledge warehouse. You’ll be able to visualize question outcomes with charts, and discover, share, and collaborate on knowledge together with your groups in SQL by a typical interface.

With SQL Notebooks, Amazon Redshift Question Editor V2.0 simplifies organizing, documenting, and sharing of knowledge evaluation with SQL queries. The pocket book interface permits customers similar to knowledge analysts, knowledge scientists, and knowledge engineers to writer SQL code extra simply, organizing a number of SQL queries and annotations on a single doc. You can even collaborate together with your workforce members by sharing notebooks. With SQL Notebooks, you may visualize the question outcomes utilizing charts. SQL Notebooks assist offers another technique to embed all queries required for a whole knowledge evaluation in a single doc utilizing SQL cells. Question Editor V2.0 simplifies improvement of SQL notebooks with question versioning and export/import options. You should use the built-in model historical past characteristic to trace modifications in your SQL and markdown cells. With the export/import characteristic, you may simply transfer your notebooks from improvement to manufacturing accounts or share with workforce members cross-Area and cross-account.

On this put up, we display find out how to use SQL Notebooks utilizing Question Editor V2.0 and stroll you thru among the new options.

Use instances for SQL Notebooks

Clients need to use SQL notebooks when they need reusable SQL code with a number of SQL statements and annotations or documentations. For instance:

  • A knowledge analyst may need a number of SQL queries to research knowledge that create momentary tables, and runs a number of SQL queries in sequence to derive insights. They could additionally carry out visible evaluation of the outcomes.
  • A knowledge scientist may create a pocket book that creates some coaching knowledge, creates a mannequin, exams the mannequin, and runs pattern predictions.
  • A knowledge engineer may need a script to create schema and tables, load pattern knowledge, and run take a look at queries.

Answer overview

For this put up, we use the International Database of Occasions, Language, and Tone (GDELT) dataset, which displays information the world over, and the information is saved for each second of day-after-day. This data is freely accessible as a part of the Registry of Open Information on AWS.

For our use case, a knowledge scientist desires to carry out unsupervised studying with Amazon Redshift ML by making a machine studying (ML) mannequin, after which generate insights from the dataset, create a number of variations of the pocket book, visualize utilizing charts, and share the pocket book with different workforce members.


To make use of the SQL Notebooks characteristic, you need to add a coverage for SQL Notebooks to a principal—an AWS Identification and Entry Administration (IAM) consumer or position—that already has one of many Question Editor V2.0 managed insurance policies. For extra data, see Accessing the question editor V2.0.

Import the pattern pocket book

To import the pattern SQL pocket book in Question Editor V2.0, full the next steps:

  1. Obtain the pattern SQL pocket book.
  2. On the Amazon Redshift console, select Question Editor V2 within the navigation pane. Question Editor V2.0 opens in a brand new browser tab.
  3. To connect with a database, select the cluster or workgroup title.
  4. If prompted, enter your connection parameters.  For extra details about totally different authentication strategies, consult with Connecting to an Amazon Redshift database.
  5. Whenever you’re related to the database, select Notebooks within the navigation pane.
  6. Select Import to make use of the SQL pocket book downloaded in step one.
    After the pocket book is imported efficiently, will probably be accessible beneath My notebooks.
  7. To open the pocket book, right-click on the pocket book and select Open pocket book, or double-click on the pocket book.

Carry out knowledge evaluation

Let’s discover how one can run totally different queries from the SQL pocket book cells to your knowledge evaluation.

  1. Let’s begin by creating the desk.
  2. Subsequent, we load knowledge into the desk utilizing COPY command. Earlier than operating the COPY command within the pocket book, you’ll want to have a default IAM position hooked up to your Amazon Redshift cluster, or exchange the default key phrase with the IAM position ARN hooked up to the Amazon Redshift cluster:
    COPY gdelt_data FROM 's3://gdelt-open-data/occasions/1979.csv'
    area 'us-east-1' iam_role 'arn:aws:iam::<account-id>:position/<role-name>' csv delimiter 't';

    For extra data, consult with Creating an IAM position as default in Amazon Redshift.

    Earlier than we create the ML mannequin, let’s study the coaching knowledge.

  3. Earlier than you run the cell to create the ML mannequin, exchange the <your-amazon-s3-bucket-name> with the S3 bucket of your account to retailer intermediate outcomes.
  4. Create the ML mannequin.
  5. To verify the standing of the mannequin, run the pocket book cell Present standing of the mannequin.  The mannequin is prepared when the Mannequin State key worth is READY.
  6. Let’s determine the clusters related to every GlobalEventId.
  7. Let’s get insights into the information factors assigned to one of many clusters.

Within the previous screenshot, we will observe the information factors assigned to the clusters. We see clusters of occasions comparable to interactions between the US and China (most likely as a result of institution of diplomatic relations), between the US and RUS (most likely comparable to the SALT II Treaty), and people involving Iran (most likely comparable to the Iranian Revolution).

So as to add textual content and format the looks to offer context and extra data to your knowledge evaluation duties, you may add a markdown cell. For instance, in our pattern pocket book, we now have supplied an outline in regards to the question within the markdown cells to make it less complicated to know. For extra data on markdown cells, consult with Markdown Cells.

To run all of the queries within the SQL pocket book without delay, select Run all.

Add new SQL and markdown cells

So as to add new SQL queries or markdown cells, full the next steps:

  1. After you open the SQL pocket book, hover over the cell and select Insert SQL so as to add a SQL cell or Insert markdown so as to add a markdown cell.
  2. The brand new cell is added earlier than the cell you chose.
  3. You can even transfer the brand new cell after a selected cell by selecting the up or down icon.

Visualize pocket book outcomes utilizing charts

Now you could run the SQL pocket book cell and get the outcomes, you may show a graphic visualization of the outcomes through the use of the chart choice in Question Editor V2.0.

Let’s run the next question to get extra insights into the information factors assigned to one of many cluster’s outcomes and visualize utilizing charts.

To visualise the question outcomes, configure a chart on the Outcomes tab. Select actor2name for the X-axis and totalarticles for the Y-axis dropdown. By default, the graph kind is a bar chart.

Charts might be plotted in each cell, and every cell can have a number of outcome tables, however solely one among them can have a chart. For extra details about working with charts in Question Editor V2.0, consult with Visualizing question outcomes.

Versioning in SQL Notebooks

Model management permits simpler collaboration together with your friends and reduces the dangers of any errors. You’ll be able to create a number of variations of the identical SQL pocket book through the use of the Save model choice in Question Editor V2.0.

  1. Within the navigation pane, select Notebooks.
  2. Select the SQL pocket book that you just need to open.
  3. Select the choices menu (three dots) and select Save model.

    SQL Notebooks creates the brand new model and shows a message that the model has been created efficiently.

    Now we will view the model historical past of the pocket book.
  4. Select the SQL pocket book for which you created the model (right-click) and select Model historical past.

    You’ll be able to see a listing of all of the variations of the SQL pocket book.
  5. To revert to a selected model of the pocket book, select the model you need and select Revert to model.
  6. To create a brand new pocket book from a model, select the model you need and select Create a brand new pocket book from the model.

Duplicate the SQL pocket book

Whereas working together with your friends, you may have to share your pocket book, however you additionally have to proceed making modifications in your pocket book. To keep away from any impression with the shared model, you may duplicate the pocket book and maintain working in your modifications within the duplicate copy of the pocket book.

  1. Within the navigation pane, select Notebooks.
  2. Open the SQL pocket book.
  3. Select the choices menu (three dots) and select Duplicate.
  4. Present the duplicate pocket book title.
  5. Select Duplicate.

Share notebooks

You usually have to collaborate with different groups, for instance to share the queries for integration testing, deploy the queries from dev to the manufacturing account, and extra. You’ll be able to obtain this by sharing the pocket book together with your workforce.

A workforce is outlined for a set of customers who collaborate and share Question Editor V2.0 sources. An administrator can create a workforce by including a tag to an IAM position.

Earlier than you begin sharing your pocket book together with your workforce, just be sure you have the principal tag sqlworkbench-team set to the identical worth as the remainder of your workforce members in your account. For instance, an administrator may set the worth to accounting-team for everybody within the accounting division. To create a workforce and tag, consult with Permissions required to make use of the question editor v2.0.

To share a SQL pocket book with a workforce in the identical account, full the next steps:

  1. Open the SQL pocket book you need to share.
  2. Select the choices menu (three dots) and select Share with my workforce.Notebooks which are shared to the workforce might be seen within the notebooks panel’s Shared to my workforce tab, and the notebooks which are shared by the consumer might be seen in Shared by me tab.You can even use the export/import characteristic for different use instances. For instance, builders can deploy notebooks from decrease environments to manufacturing, or prospects can present a SAAS resolution sharing pocket book with their end-users in several accounts or Areas. Full the next steps to export and import SQL notebooks:
  3. Open the SQL pocket book you need to share.
  4. Select the choices menu (three dots) and select Export. SQL Notebooks saves the pocket book in your native desktop as a .ipynb file.
  5. Import the pocket book into one other account or Area.

Run parameterized queries in a SQL pocket book

Database customers usually have to go parameters to the queries with totally different values at runtime. You’ll be able to obtain this in SQL Notebooks through the use of parameterized queries. It may be outlined within the question as ${parameter_name}, and when the question is run, it prompts to set a price for the parameter.

Let’s have a look at the next instance, wherein we go the events_cluster parameter.

  1. Insert a SQL cell within the SQL pocket book and add the next SQL question:
    choose news_monitoring_cluster ( AvgTone, EventCode, NumArticles, Actor1Geo_Lat, Actor1Geo_Long, Actor2Geo_Lat, Actor2Geo_Long ) as events_cluster, eventcode, actor1name, actor2name, sum(numarticles) as totalarticles
    from gdelt_data
    the place events_cluster = ${events_cluster}
    and actor1name <> ' 'and actor2name <> ' '
    group by 1,2,3,4
    order by 5 desc

  2. When prompted, enter the worth of the parameter events_cluster, (for this put up, we set the worth as 4).
  3. Select Run now to run the question.

The next screenshot reveals the question outcomes with the events_cluster parameter worth set to 4.


On this put up, we launched SQL Notebooks utilizing the Amazon Redshift Question Editor V2.0. We used a pattern pocket book to display the way it simplifies knowledge evaluation duties for a knowledge scientist and how one can collaborate utilizing notebooks together with your workforce.

In regards to the Authors

Ranjan Burman is an Analytics Specialist Options Architect at AWS. He focuses on Amazon Redshift and helps prospects construct scalable analytical options. He has greater than 15 years of expertise in several database and knowledge warehousing applied sciences. He’s keen about automating and fixing buyer issues with using cloud options.

Erol Murtezaoglu, a Technical Product Supervisor at AWS, is an inquisitive and enthusiastic thinker with a drive for self-improvement and studying. He has a robust and confirmed technical background in software program improvement and structure, balanced with a drive to ship commercially profitable merchandise. Erol extremely values the method of understanding buyer wants and issues with a purpose to ship options that exceed expectations.

Cansu Aksu is a Frontend Engineer at AWS. She has a number of years of expertise in constructing consumer interfaces that simplify advanced actions and contribute to a seamless buyer expertise. In her profession in AWS, she has labored on totally different points of net software improvement, together with entrance finish, backend, and software safety.

Andrei Marchenko is a Full Stack Software program Improvement Engineer at AWS. He works to convey notebooks to life on all fronts—from the preliminary necessities to code deployment, from the database design to the end-user expertise. He makes use of a holistic method to ship one of the best expertise to prospects.

Debu-PandaDebu Panda is a Senior Supervisor, Product Administration at AWS. He’s an business chief in analytics, software platform, and database applied sciences, and has greater than 25 years of expertise within the IT world. Debu has revealed quite a few articles on analytics, enterprise Java, and databases and has offered at a number of conferences similar to re:Invent, Oracle Open World, and Java One. He’s lead writer of the EJB 3 in Motion (Manning Publications 2007, 2014) and Middleware Administration (Packt, 2009)


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