Big Data

Speak to your knowledge: Question your knowledge lake with Amazon QuickSight Q

Amazon QuickSight Q makes use of machine studying (ML) and pure language expertise to empower you to ask enterprise questions on your knowledge and get solutions immediately. You possibly can merely enter your questions (for instance, “What’s the year-over-year gross sales development?”) and get the reply in seconds within the type of a QuickSight visible.

Some enterprise questions can’t be answered by current enterprise intelligence (BI) dashboards. It could possibly take days or perhaps weeks for the BI staff to accommodate these wants and refine their resolution. As a result of Q doesn’t rely on prebuilt dashboards or studies to reply questions, it removes the necessity for BI groups to create or replace dashboards each time a brand new enterprise query arises. You possibly can ask questions and obtain solutions within the type of visuals in seconds straight from inside QuickSight or from net purposes and portals. Q empowers each enterprise consumer to self-serve and get insights quicker, no matter their background or skillset.

On this publish, we stroll you thru the steps to configure Q utilizing an Olympic Video games public dataset and reveal how an end-user can ask easy questions straight from Q in an interactive method and obtain solutions in seconds.

You possibly can interactively play with the Olympic dashboard and Q search bar within the following interactive demo.

Resolution overview

We use Olympic video games public datasets to configure a Q matter and talk about ideas and tips on learn how to make additional configurations on the subject that allow Q to supply immediate solutions utilizing ML-powered, pure language question (NLQ) capabilities that empower you to ask questions on knowledge utilizing on a regular basis enterprise language.

The video from Information Con LA offers a high-level demonstration of the capabilities lined on this publish.

Moreover, we talk about the next:

  • Greatest practices for knowledge modeling of a Q matter
  • Learn how to carry out knowledge cleaning utilizing AWS Glue DataBrew, SQL, or an Amazon SageMaker Jupyter pocket book on datasets to construct a Q matter

We use a number of publicly accessible datasets from Kaggle. The datasets have historic details about athletes, together with identify, ID, age, weight, nation, and medals.

We use the 2020 Olympic datasets and historic knowledge. We additionally use the datasets Introduction of Girls Olympic Sport and Girls of Olympic Video games to find out the participation of ladies athletes in Olympics and uncover developments. The QuickSight datasets created utilizing these public knowledge information are added to a Q matter, as proven within the following screenshot. We offer particulars on creating QuickSight datasets later on this publish.


To comply with together with the answer introduced on this publish, you could have entry to the next:

Create resolution sources

The general public datasets in Kaggle can’t be straight utilized to create a Q matter. Now we have already cleansed the uncooked knowledge and have offered the cleansed datasets within the GitHub repo. In case you are all in favour of studying extra about knowledge cleaning, we mentioned three totally different knowledge cleaning strategies on the finish of this publish.

To create your sources, full the next steps:

  1. Create an S3 bucket known as olympicsdata.
  2. Create a folder for every knowledge file, as proven within the following screenshot.
  3. Add the information information from the GitHub repo into their respective folders.
  4. Deploy the offered CloudFormation template and supply the required data.

The template creates an Athena database and tables, as proven within the following screenshot.

The template additionally creates the QuickSight knowledge supply athena-olympics and datasets.

Create datasets in QuickSight

To construct the Q matter, we have to mix the datasets, as a result of every desk incorporates solely partial knowledge. Becoming a member of these tables helps reply questions throughout all of the options of the 2020 Olympics.

We create the Olympics 2021 dataset by becoming a member of the tables Medals_athletes_2021, Athletes_full_2021, Coach_full_2021, and Tech_official_2021.

The next screenshot reveals the joins for our full dataset.

Medals_athletes_2021 is the principle desk, with the next be part of situations:

  • Left outer be part of athletes_full_2021 on athlete_name, discipline_code, and country_code
  • Left outer be part of coach_full_2021 on nation, self-discipline, and occasion
  • Left outer be part of tech_official_2021 on self-discipline

Lastly, we have now the next datasets that we use for our Q matter:

  • Olympics 2021 Particulars
  • Medals 2021
  • Olympics Historical past (created utilizing the Olympics desk)
  • Introduction of Girls Olympics Sports activities
  • Girls within the Olympic Motion

Create a Q matter

Matters are collections of a number of datasets that symbolize a topic space that what you are promoting customers can ask questions on. In QuickSight, you’ll be able to create and handle subjects on the Matters web page. Whenever you create a subject, what you are promoting customers can ask questions on it within the Q search bar.

Whenever you create subjects in Q, you’ll be able to add a number of datasets to them after which configure all of the fields within the datasets to make them pure language-friendly. This permits Q to supply what you are promoting customers with the right visualizations and solutions to their questions.

The next are knowledge modeling greatest practices for Q subjects:

  • Cut back the variety of datasets by consolidating the information. Any given query can solely hit one knowledge set, so solely embody a number of datasets if they’re associated sufficient to be a part of the identical matter, however distinct sufficient you can ask a query towards them independently.
  • For naming conventions, present a significant identify or alias (synonym) of a discipline to permit the end-user to simply question it.
  • If a discipline seems in numerous datasets, be sure that this discipline has the identical identify throughout totally different datasets.
  • Validate knowledge consistency. For instance, the overall worth of a metric that aggregates from totally different datasets needs to be constant.
  • For fields that don’t request on-the-fly calculations, for instance, metrics with distributive capabilities (sum, max, min, and so forth), push down the calculation into an information warehouse.
  • For fields that request on-the-fly calculations, create the calculated discipline within the QuickSight dataset or Q matter. If different subjects or dashboards would possibly reuse the identical discipline, create it within the datasets.

To create a subject, full the next steps:

  1. On the QuickSight console, select Matters within the navigation pane.
  2. Select New matter.
  3. For Subject identify, enter a reputation.
  4. For Description, enter an outline.
  5. Select Save.
  6. On the Add knowledge to matter web page that opens, select Datasets, after which choose the datasets that we created within the earlier part.
  7. Select Add knowledge to create the subject.

Improve the subject

On this part, we talk about numerous methods you can improve the subject.

Add calculated fields to a subject dataset

You possibly can add new fields to a dataset in a subject by creating calculated fields.

For instance, we have now the column Age in our Olympics dataset. We are able to create a calculated discipline to group age into totally different ranges utilizing the ifelse perform. This calculated discipline will help us ask a query like “What number of athletes for every age group?”

  1. Select Add calculated discipline.
  2. Within the calculation editor, enter the next syntax:
    Age<=20, '0-20',
    Age>20 and Age <=40, '21-40',
    Age>40 and Age<=60, '41-60',

  3. Identify the calculated discipline Age Teams.
  4. Select Save.

The calculated discipline is added to the listing of fields within the matter.

Add filters to a subject dataset

Let’s say lot of research is predicted on the dataset for the summer time season. We are able to add a filter to permit for straightforward number of this worth. Moreover, if we wish to enable evaluation towards knowledge for the summer time season solely, we will select to all the time apply this filter or apply it because the default alternative, however enable customers to ask questions on different seasons as nicely.

  1. Select Add filter.
  2. For Identify, enter Summer time.
  3. Select the Girls within the Olympic Motion dataset.
  4. Select the Olympics Season discipline.
  5. Select Customized filter listing for Filter kind and set the rule as embody.
  6. Enter Summer time underneath Values.
  7. Select Apply all the time, except a query leads to an express filter from the dataset.
  8. Select Save.

The filter is added to the listing of fields within the matter.

Add named entities to a subject dataset

We are able to outline named entities if we have to present customers a mixture of fields. For instance, when somebody asks for participant particulars, it is sensible to point out them participant identify, age, nation, sport, and medal. We are able to make this occur by defining a named entity.

  1. Select Add named entity.
  2. Select the Olympics dataset.
  3. Enter Participant Profile for Identify.
  4. Enter Info of Participant for Description.
  5. Select Add discipline.
  6. Select Participant Identify from the listing.
  7. Select Add discipline once more and add the fields Age, International locations, Sport, and Medal.
    The fields listed are the order they seem in solutions. To maneuver a discipline, select the six dots subsequent to the identify and drag and drop the sphere to the order that you really want.
  8. Select Save.

The named entity is added to the listing of fields within the matter.

Make Q subjects pure language-friendly

To assist Q interpret your knowledge and higher reply your readers’ questions, present as a lot details about your datasets and their related fields as potential.

To make the subject extra pure language-friendly, use the next procedures.

Rename fields

You can also make your discipline names extra user-friendly in your subjects by renaming them and including descriptions.

Q makes use of discipline names to grasp the fields and hyperlink them to phrases in your readers’ questions. When your discipline names are user-friendly, it’s simpler for Q to attract hyperlinks between the information and a reader’s query. These pleasant names are additionally introduced to readers as a part of the reply to their query to supply further context.

Let’s rename the beginning date discipline from the athlete dataset as Athlete Beginning Date. As a result of we have now a number of beginning date fields within the subjects for coach, athlete, and tech roles, renaming the athletes’ beginning date discipline helps Q simply hyperlink to the information discipline after we ask questions concerning athletes’ beginning dates.

  1. On the Fields web page, select the down arrow at far proper of the Beginning Date discipline to increase it.
  2. Select the pencil icon subsequent to the sphere identify.
  3. Rename the sphere to Athlete Beginning Date.

Add synonyms to fields in a subject

Even for those who replace your discipline names to be user-friendly and supply an outline for them, your readers would possibly nonetheless use totally different names to discuss with them. For instance, a participant identify discipline is perhaps known as participant, gamers, or sportsman in your reader’s questions.

To assist Q make sense of those phrases and map them to the right fields, you’ll be able to add a number of synonyms to your fields. Doing this improves Q’s accuracy.

  1. On the Fields web page, underneath Synonyms, select the pencil icon for Participant Identify.
  2. Enter participant and sportsman as synonyms.

Add synonyms to discipline values

Like we did for discipline names, we will add synonyms for class values as nicely.

  1. Select the Gender discipline’s row to increase it.
  2. Select Configure worth synonyms, then select Add.
  3. Select the pencil icon subsequent to the F worth.
  4. Add the synonym Feminine.
  5. Repeat these steps so as to add the synonym Male for M.
  6. Select Finished.

Assign discipline roles

Each discipline in your dataset is both a dimension or a measure. Understanding whether or not a discipline is a dimension or a measure determines what operations Q can and might’t carry out on a discipline.

For instance, setting the sphere Age as a dimension implies that Q doesn’t attempt to mixture it because it does measures.

  1. On the Fields web page, increase the Age discipline.
  2. For Position, select Dimension.

Set discipline aggregations

Setting discipline aggregations tells Q which perform ought to or shouldn’t be used when these fields are aggregated throughout a number of rows. You possibly can set a default aggregation for a discipline, and specify aggregations that aren’t allowed.

A default aggregation is the aggregation that’s utilized when there’s no express aggregation perform talked about or recognized in a reader’s query. For instance, let’s ask Q “Present whole variety of occasions.” On this case, Q makes use of the sphere Complete Occasions, which has a default aggregation of Sum, to reply the query.

  1. On the Fields web page, increase the Complete Occasions discipline.
  2. For Default aggregation, select Sum.
  3. For Not allowed aggregation, select Common.

Specify discipline semantic varieties

Offering extra particulars on the sphere context will assist Q reply extra pure language questions. For instance, customers would possibly ask “Who gained essentially the most medals?” We haven’t set any semantic data for any fields in our dataset but, so Q doesn’t know what fields to affiliate with “who.” Let’s see how we will allow Q to deal with this query.

  1. On the Fields web page, increase the Participant Identify discipline.
  2. For Semantic Kind, select Particular person.

This permits Q to floor Participant Identify as an possibility when answering “who”-based questions.

Exclude unused or pointless fields

Fields from all included datasets are displayed by default. Nevertheless, we have now just a few fields like Brief identify of Nation, URL Coach Full 2021, and URL Tech Official 2021 that we don’t want in our matter. We are able to exclude pointless fields from the subject to stop them from exhibiting up in outcomes by selecting the slider subsequent to every discipline.

Ask questions with Q

After we create and configure our matter, we will now work together with Q by getting into questions within the Q search bar.

For instance, let’s enter present whole medals by nation. Q presents a solution to your query as a visible.

You possibly can see how Q interpreted your query within the description on the visible’s higher left. Right here you’ll be able to see the fields, aggregations, matter filters, and datasets used to reply the query. The subject filter na is utilized on the Medal attribute, which excludes na values from the aggregation. For extra data on matter filters, see Including filters to a subject dataset.

Q shows the outcomes utilizing the visible kind greatest suited to convey the knowledge. Nevertheless, Q additionally provides you the flexibleness to view leads to different visible varieties by selecting the Visible icon.

One other instance, let’s enter who's the oldest participant in basketball. Q presents a solution to your query as a visible.

Generally Q won’t interpret your query the way in which you wished. When this occurs, you’ll be able to present suggestions on the reply or make ideas for corrections to the reply. For extra details about offering reply suggestions, see Offering suggestions about QuickSight Q subjects. For extra details about correcting solutions, see Correcting mistaken solutions offered by Amazon QuickSight Q.


On this publish, we confirmed you learn how to configure Q utilizing an Olympic video games public dataset and so end-users can ask easy questions straight from Q in an interactive method and obtain solutions in seconds. If in case you have any suggestions or questions, please go away them within the feedback part.

Appendix 1: Varieties of questions supported by Q

Let’s have a look at samples of every query kind that Q can reply utilizing the subject created earlier on this publish.

Attempt the next questions or your personal questions and proceed enhancing the subject to enhance accuracy of responses.

Query Kind Instance
Dimensional Group Bys present whole medals by nation
Dimensional Filters (Embody) present whole medals for usa
Date Group Bys present yearly development of ladies members
Multi Metrics variety of ladies occasions in comparison with whole occasions
KPI-Based mostly Interval over Durations (PoPs) what number of ladies members in 2018 over 2016
Relative Date Filters present whole medals for usa within the final 5 years
Time Vary Filters listing of ladies sports activities launched since 2016
Prime/Backside Filter present me the highest 3 participant with gold medal
Type Order present prime 3 nations with most medals
Combination Metrics Filter present groups that gained greater than 50 medals
Checklist Questions listing the ladies sports activities by 12 months through which they’re launched
OR filters Present participant who bought gold or silver medal
% of Complete Proportion of gamers by nation
The place Questions the place are essentially the most variety of medals
When Questions when ladies volleyball launched into olympic video games
Who Questions who’s the oldest participant in basketball
Exclude Questions present nations with highest medals excluding usa

Appendix 2: Information cleaning

On this part, we offer three choices for knowledge cleaning: SQL, DataBrew, and Python.

Choice 1: SQL

For our first possibility, we talk about learn how to create Athena tables on the downloaded Excel or CSV information after which carry out the information cleaning utilizing SQL. This feature is appropriate for many who use Athena tables as an information supply for QuickSight datasets and are snug utilizing SQL.

The SQL queries to create Athena tables can be found within the GitHub repo. In these queries, we carry out knowledge cleaning by renaming, altering the information kind of some columns, in addition to eradicating the duplicates of rows. Correct naming conventions and correct knowledge varieties assist Q effectively hyperlink the inquiries to the information fields and supply correct solutions.

Use the next pattern DDL question to create an Athena desk for women_introduction_to_olympics:

CREATE EXTERNAL TABLE women_introduction_to_olympics(
12 months string,
sport string)
's3://<<s3 bucket identify>>/womeninolympics/introduction_of_women_olympic_sports'

In our knowledge information, there are few columns which can be frequent throughout a couple of dataset which have totally different column names. For instance, gender is obtainable as gender or intercourse, nation is obtainable as nation or staff or staff/noc, and individual names have a job prefix in a single dataset however not in different datasets. We rename such columns utilizing SQL to take care of constant column names.

Moreover, we have to change different demographic columns like age, top, and weight to the INT knowledge kind, in order that they don’t get imported as String.

The next columns from the information information have been remodeled utilizing SQL.

Information File Unique Column New Column
medals Self-discipline
Medal_date (timestamp)
Medal_date (date)
Athletes identify
Coaches identify
Athlete_events (historical past) Crew
Age (String)
Top (String)
Weight (String)
Age (Integer)
Top (Integer)
Weight (Integer)

Choice 2: DataBrew

On this part, we talk about an information cleaning possibility utilizing DataBrew. DataBrew is a visible knowledge preparation software that makes it straightforward to wash and put together knowledge with no prior coding information. You possibly can straight load the outcomes into an S3 bucket or load the information by importing an Excel or CSV file.

For our instance, we stroll you thru the steps to implement knowledge cleaning on the medals_athletes_2021 dataset. You possibly can comply with the identical course of to carry out any needed knowledge cleansing on different datasets as nicely.

Create a brand new dataset in DataBrew utilizing medals_athletes.csv after which create a DataBrew venture and implement the next recipes to cleanse the information within the medals_athletes_2021 dataset.

  1. Delete empty rows within the athlete_name column.
  2. Delete empty rows within the medal_type column.
  3. Delete duplicate rows within the dataset.
  4. Rename self-discipline to Sport.
  5. Delete the column discipline_code.
  6. Break up the column medal_type on a single delimiter.
  7. Delete the column medal_type_2, which was created on account of step 6.
  8. Rename medal_type_1 to medal_type.
  9. Change the information kind of column medal_date from timestamp to date.

After you create the recipe, publish it and create a job to output the leads to your required vacation spot. You possibly can create QuickSight SPICE datasets by importing the cleaned CSV file.

Choice 3: Python

On this part, we talk about knowledge cleaning utilizing NumPy and Pandas of Python on the medals_athletes_2021 dataset. You possibly can comply with the identical course of to carry out any needed knowledge cleaning on different datasets as nicely. The pattern Python code is obtainable on GitHub. This feature is appropriate for somebody who’s snug processing the information utilizing Python.

  1. Delete the column discipline_code:

  2. Rename the column self-discipline to sport:
    olympic.rename(columns={'self-discipline': 'sport'})

You possibly can create QuickSight SPICE datasets by importing the cleansed CSV.

Appendix 3: Information cleaning and modeling within the QuickSight knowledge preparation layer

On this part, we talk about another methodology of knowledge cleaning you can carry out from the QuickSight knowledge preparation layer, along with the strategies mentioned beforehand. Utilizing SQL, DataBrew, or Python have benefits as a result of you’ll be able to put together and clear the information exterior QuickSight so different AWS companies can use the cleansed outcomes. Moreover, you’ll be able to automate the scripts. Nevertheless, Q authors need to study different instruments and programming languages to reap the benefits of these choices.

Cleaning knowledge within the QuickSight dataset preparation stage permits non-technical Q authors to construct the applying finish to finish in QuickSight with a codeless methodology.

The QuickSight dataset shops any knowledge preparation finished on the information, in order that the ready knowledge could be reused in a number of analyses and subjects.

Now we have offered just a few examples for knowledge cleaning within the QuickSight knowledge preparation layer.

Change a discipline identify

Let’s change the identify knowledge discipline from Athletes_full_2021 to athlete_name.

  1. Within the knowledge preview pane, select the edit icon on the sphere that you simply wish to change.
  2. For Identify, enter a brand new identify.
  3. Select Apply.

Change a discipline knowledge kind

You possibly can change the information kind of any discipline from the information supply within the QuickSight knowledge preparation layer utilizing the next process.

  1. Within the knowledge preview pane, select the edit icon on the sphere you wish to change (for instance, birth_date).
  2. Select Change knowledge kind and select Date.

This converts the string discipline to a date discipline.

Appendix 4: Details about the tables

The next desk illustrates the scope of every desk within the dataset.

In regards to the authors

Ying Wang is a Supervisor of Software program Improvement Engineer. She has 12 years expertise in knowledge analytics and knowledge science. In her knowledge architect life, she helped buyer on enterprise knowledge structure options to scale their knowledge analytics within the cloud. Presently, she helps buyer to unlock the ability of Information with QuickSight from engineering/product by delivering new options.

Ginni Malik is a Information & ML Engineer with AWS Skilled Providers. She assists clients by architecting enterprise stage knowledge lake options to scale their knowledge analytics within the cloud. She is a journey fanatic and likes to run half-marathons.

Niharika Katnapally is a QuickSight Enterprise Intelligence Engineer with AWS Skilled Providers. She assists clients by creating QuickSight dashboards to assist them achieve insights into their knowledge and make knowledge pushed enterprise choices.

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