{"data":{"entities":{"widgets":{"9eb86874-79f4-4626-b147-5bf4f4315af9":{"kind":"popup","cta":"NEXT","audio_url":"","options":{"root":{"theme":"light","close_button":true,"prev_button":true,"show_step_number":true,"spot":"hard","hasSpotlight":false,"spotlightColor":"#9939eb","backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":650,"secondaryButton":false,"maxFrameWidth":null},"description":{"value":"

Welcome to Imply Polaris

Discover how you can build real-time analytics applications faster, more affordably, and with less effort.

Take a guided tour to explore Polaris, a Druid-as-a-Service, at your own pace. We cover everything from data ingestion to visualization.

Click \"NEXT\" to start the interactive tour.

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Load data

Batch ingest from files, push events using the Polaris API, or ingest from streaming sources including Amazon S3, Kafka, and Confluent Cloud. Polaris can auto-create tables when you ingest data.

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Analyze and Visualize Data

Explore, analyze and query your data with visually rich, easy-to-use tools including data cubes, dashboards, reports, and alerts.

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Create collections

Create collections to group Polaris assets (such as tables, alerts, and data cubes) together. Save your favorite assets for easy access.

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Manage projects

You can create multiple projects and size them to meet your specific data and cost requirements.

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Thanks for taking a quick tour of the dashboard

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Create a Data Source

Ingest data from a variety of sources, including uploaded files, Amazon S3 buckets, and Kafka topics. Polaris automatically optimizes ingestion tasks within your project's performance.

Click \"NEXT\" to get a simple guide for creating data sources for both streaming and batch data.

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In order to ingest data, we first need to create a connection.

Click \"Sources\" to add a data source.

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Click \"Create Source\" to see the available options.

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Let's first explore options for streaming

Apache Kafka and MSK: Ingest streaming event data from Apache Kafka from a self-managed Apache Kafka cluster or Amazon MSK
Amazon Kinesis: Ingest data from Amazon Kinesis Data Streams
Confluent Cloud: Ingest streaming event data from Confluent Cloud

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Select \"Confluent Cloud\" as the connection we want to establish.

Note: The following characteristics apply to ingestion from Confluent Cloud to Polaris:

Exactly once guarantees.
Occurs across the Internet with TLS encryption and SASL authentication.

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Click into \"Connection name\" and we will add RT_Thermostat_Data as the connection name for you.

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Click into \"Topic name\" and we will add rt_thermo as the name of the Confluent Cloud topic that contains the event data in the Kafka cluster.

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Click into \"Bootstrap server\" and we will add the address of the host and port pairs of the brokers in the Kafka cluster for you.

Note: For details on where to find the bootstrap server in Confluent Cloud, see \"Access cluster settings\" in your Confluent Cloud Console.

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Click into \"Confluent API key\" and \"Confluent API key secret\" and we will add the API key details for access to Kafka resources in Confluent Cloud.

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Click \"Create connection\" and we will define this as an ingestion source for you.

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A connection to Confluent Cloud with the name \"RT_Thermostat_Data\" has now been established.

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Once again, click \"Create Source\" to see the available options.

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Now, let's explore the options for batch data

Amazon S3: Read files from Amazon S3 buckets
Files: Upload files to the Polaris staging area
Tables: Load data from one Polaris table into another table

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Click on \"Select files from computer\" to upload a file containing six months of thermostat data.

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The file staging area displays the name, size, and upload date of the file.

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Thank you for exploring how to create a Data Source

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Create a Table

Polaris uses tables to store and display data records. Data is organized in rows and columns.

Click \"Next\" to get a basic walkthrough of creating a table.

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Click \"Tables\" to add a table to Polaris.

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Click \"Create table.\"

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Click into \"Name\" and we will add IoT Thermastat Data as the name of your table for you.

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Polaris supports the following types of tables

Detail (the default type): Stores each event as it is ingested, without any pre-aggregation.
Aggregate: Groups individual records according to the table's time granularity and dimensions.

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The choice of schema mode depends on data governance requirements.

Flexible (the default mode): No need to declare any columns before ingestion. The table adapts as incoming data changes (i.e. adding new fields).
Strict: All columns in the table are defined and any incoming data that does not match the table will be rejected.

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Click \"Next\" to accept the default options.

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In every schema, a timestamp dimension is automatically included. For Flexible tables, Polaris creates the primary timestamp column \"__time.\"

This timestamp is used for data partitioning, sorting, and time-based operations.

Note: If your dataset lacks a timestamp, Polaris will assign the ingestion time as the timestamp.

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During the ingestion process, Polaris automatically detects and incorporates any additional data or inputs, determining the necessary dimensions and their respective column data types.\"

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Thank you for exploring how to create a Table

","color":"#F3F5F7"},"cta":{"bold":true,"fontSize":18,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":"","extUrl":null,"flowId":"4f234588-d1e4-460b-a194-b850f28b475f","widgetId":null,"extUrlBlank":true},"secondary":{"value":"Try for Free","bold":true,"fontSize":18,"italic":false,"textUnderline":false,"color":"#ffffff","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":"","extUrl":"https://signup.imply.io/get-started?_ga=2.125427977.2005099056.1698070620-2125054808.1683207288&_gac=1.194311775.1696266434.Cj0KCQjw1OmoBhDXARIsAAAYGSE_AalZeMgFAYxnt1TJQMcE_IkzlRcz39GjeNyPLAgyh-HWCyXkGtMaAkj9EALw_wcB","extUrlBlank":true},"js":{"value":""},"version":1,"title":{"color":"#F3F5F7"}},"id":"1b774c70-0fa4-476a-94ba-7b4b167e4699","title":"","dom_id":".c-BaseHeaderVariants-irmpVq-gBTOli-columnType-undeclared > .c-EditAnchor-clezyk > .c-HeaderCell-eeqcJN","page_id":"9575fa60-9b37-41f6-a614-a5c4e7f2aef6","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"5db90184-a37d-4c48-9dd1-7993ca6b508f"},"c5ec2f83-557a-4a22-acb7-a7259b18cd9d":{"kind":"popup","cta":"NEXT","audio_url":"","options":{"root":{"theme":"light","close_button":false,"prev_button":true,"show_step_number":true,"spot":"hard","hasSpotlight":false,"spotlightColor":"#9939eb","backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":700,"secondaryButton":false,"maxFrameWidth":null},"description":{"value":"

Load Data

Launch an ingestion job to load data into a table. The table and schema are automatically created by loading the data.

Click \"NEXT\" to get a basic walkthrough of creating an ingestion job for both streaming and batch data.

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Click \"Load data\" to ingest data into the table called IoT Thermostat Data.

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Click \"Confluent Cloud\" as the source of data for ingestion.

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Next, click \"RT_Thermostat_Data\" as the connection from Confluent Cloud.

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Click \"Next\" to continue.

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Polaris parses your data and displays a sample output.

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Polaris offers flexible table schema auto-discovery during data ingestion. It identifies required dimensions and their column data types based on the ingested data.

You have the flexibility to adjust data types, add missing fields, or remove unnecessary ones as needed.

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Click \"Continue.\"

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This table contains data for thermostat IoT devices with several columns. They are:

Timestamp: date and time when the data was recorded

Device: thermostat device

CO (Carbon Monoxide): level of carbon monoxide

Humidity: humidity level

Light: light or brightness

LPG (Liquefied Petroleum Gas): presence or concentration of LPG

Motion: Detected movement

Smoke: presence or concentration of smoke

Temp (Temperature): temperature data

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Click \"Start ingestion\"

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Note: The number of rows in this table will continuously increase due to the arrival of new data. The table will grow in real-time as data is ingested, and the number of rows can be very dynamic.

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Click \"Load data\" to ingest additional bath data into this table.

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Click \"Files\" as the source of data for ingestion.

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Click the JSON file containing 6 months of thermostat data.

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Click \"Next\"

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For a flexible table, Polaris auto-discovers the table schema during ingestion. Polaris identifies what dimensions need to be created and the data type for each dimension’s column based on the data it discovers during ingestion.

Note: You can correct data types, add missing input fields, or remove unnecessary fields.

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Click \"Continue.\"

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\"Auto\" signifies that Polaris will automatically define the data type and column.

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Click \"Start ingestion.\"

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Six months of batch data has been added to the IoT Thermostat Data table, complementing the continuously ingested real-time data stream.

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Thank you for exploring how to load data

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Query Data

Click \"NEXT\" to get a simple guide for building and running SQL queries.

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Click \"Query\"

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Let's perform initial exploratory analysis by examining the frequency of motion detection events recorded by the thermostat devices.

We will write the SQL query for you.

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Motion Detection Event Count

The 'motion_events' query yields the total count of rows in the 'Historical Thermo Data' table where the 'motion' column is 'true'.\"

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Click \"Run.\"

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The number of times motion was detected in the dataset is 6,113.

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Now, let's calculate the average temperature for each device in the \"Historical Thermo Data\" table and group the results by device.

We will write the query for you.

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Average Temperature per Device

The query results in a list of devices and their respective average temperatures. Each row corresponds to a unique device, and the 'average_temperature' column displays the recorded average temperature for each device.

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Click \"Run\"

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This query provides the average temperature for each device, helping identify temperature variations across devices.

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Now, let's identify devices with high smoke levels by using a subquery to compare each device's smoke level to the average smoke level.

We will write the SQL query for you.

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Devices with Elevated Smoke Levels

This query retrieves a list of devices, along with their respective temperature (\"temp\") and smoke levels (\"smoke\"), but it only includes rows where the \"smoke\" level exceeds the dataset's average smoke level.

Subquery for Average Smoke Level

Within the WHERE clause, a subquery (SELECT AVG(smoke) FROM \"Historical Thermo Data\") calculates the average smoke level across all devices in the dataset.

Comparison with Average Smoke Level

For each row in the dataset, the query checks if the \"smoke\" level for that device is greater than the calculated average smoke level. If it is, the row is included in the query result.\"

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Click \"Run.\"

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The results reveal devices with above-average smoke levels in the dataset. This can help pinpoint potential smoke-related issues or anomalies in specific devices or locations.

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Now, let's identify the devices that detect motion more frequently than the average of all devices.

We will write the SQL query for you.

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Devices Exceeding Average Motion Detection

This query lists devices and their motion event counts (\"motion_events\"). It includes only those devices with counts higher than the dataset's calculated average.

Average Motion Count Subquery:

A subquery calculates the average motion event count for devices detecting motion ('motion = 'true''). It groups data by device, counts motion events, and calculates the average.

Comparison with Average Motion Count:

The main query counts motion events for each device with \"motion = 'true'\" and compares it to the subquery's average. Devices exceeding the average are included in the results.

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Click \"Run.\"

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The results highlight devices with above-average motion detection frequencies, providing insights into device behavior, occupancy patterns, and potential motion-related anomalies.

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Finally, let's identify devices with high CO (Carbon Monoxide) levels that are above the overall average CO level.

","color":"#F3F5F7"},"cta":{"bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"right","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"secondary":{"value":"CTA","bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"right","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"meta":{"elementText":"","elementType":""},"version":1,"title":{"color":"#F3F5F7"}},"id":"b247dd03-6fdd-473e-a188-5e27ef050cd1","title":"","dom_id":".ace_content","page_id":"70b9eab5-5c17-4b58-b466-74841936e229","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"c42c7b14-45d4-42cc-9f36-138fc68b3efb"},"3cfbf024-fb3b-4822-90bd-8542b3cd0799":{"kind":"tooltip","cta":"NEXT","audio_url":"","options":{"root":{"alignment":"tc","theme":"light","close_button":true,"next_button":true,"prev_button":true,"secondaryButton":false,"show_step_number":true,"hide_arrow":false,"backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":600,"maxFrameWidth":null,"backdropRect":null,"backdropSelector":null,"offset":{"x":31.192551922537188,"y":26.601142387885552},"offsetFromOriginPx":{"x":0,"y":0},"spot":"hard","hasSpotlight":false,"spotlightColor":"#9939eb","trigger":null,"zoom":null},"description":{"value":"

Elevated CO Levels in Devices

This query provides a list of devices and their counts of high CO events (\"high_co_events\"). It only includes devices with CO levels exceeding the dataset's average CO level.

Average CO Level Subquery:

The first subquery (SELECT AVG(co) FROM \"Historical Thermo Data\") calculates the dataset's average CO level.

Average High CO Event Count Subquery:

The second subquery (SELECT AVG(co_count) FROM (SELECT device, COUNT(*) as co_count FROM \"Historical Thermo Data\" WHERE co > (SELECT AVG(co) FROM \"Historical Thermo Data\") GROUP BY device)) computes the average count of high CO events per device. It filters data for CO levels above the first subquery's average and counts events per device.

Comparison with Averages:

The main query filters data for CO levels exceeding the first subquery's average, groups data by device, and counts high CO events. Devices exceeding this count are included in the results.

","color":"#F3F5F7"},"cta":{"bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"right","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"secondary":{"value":"CTA","bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"right","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"meta":{"elementText":"","elementType":""},"version":1,"title":{"color":"#F3F5F7"}},"id":"3cfbf024-fb3b-4822-90bd-8542b3cd0799","title":"","dom_id":".ace_content","page_id":"ba60c252-ed69-4a59-a903-0f84286bf632","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"c42c7b14-45d4-42cc-9f36-138fc68b3efb"},"0cc50a24-209d-40f3-8393-746386494a55":{"kind":"hotspot","cta":"NEXT","audio_url":"","options":{"root":{"alignment":"rc","theme":"light","close_button":true,"next_button":true,"prev_button":true,"secondaryButton":false,"show_step_number":true,"hide_arrow":false,"backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":150,"maxFrameWidth":null,"backdropRect":null,"backdropSelector":null,"offset":{"x":4.0714263916015625,"y":51.21612548828125},"offsetFromOriginPx":{"x":0,"y":0},"spot":"none","hasSpotlight":false,"spotlightColor":"#9939eb","trigger":null,"auto_hide_text":true,"beaconColor":"#9939EB","beaconOpacity":1,"zoom":null},"description":{"value":"

Click \"Run.\"

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The results identify devices with consistently high CO levels above the dataset average and further filter them based on the count of high CO events. This helps pinpoint devices with potential CO-related issues or anomalies.

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Thank you for exploring how to Query Data

","color":"#F3F5F7"},"cta":{"bold":true,"fontSize":18,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":"","extUrl":null,"flowId":"352804b7-7c49-4091-85fa-ed0656d539d0","widgetId":null,"extUrlBlank":true},"secondary":{"value":"Try for Free","bold":true,"fontSize":18,"italic":false,"textUnderline":false,"color":"#ffffff","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":"","extUrl":"https://signup.imply.io/get-started?_ga=2.125427977.2005099056.1698070620-2125054808.1683207288&_gac=1.194311775.1696266434.Cj0KCQjw1OmoBhDXARIsAAAYGSE_AalZeMgFAYxnt1TJQMcE_IkzlRcz39GjeNyPLAgyh-HWCyXkGtMaAkj9EALw_wcB","extUrlBlank":true},"js":{"value":""},"version":1,"title":{"color":"#F3F5F7"}},"id":"08552e9b-d214-4964-8857-8b691079cb2b","title":"","dom_id":".rt-tbody > :nth-child(1) > .rt-tr","page_id":"d4e6d7f8-0fc6-45a3-8c53-de9d8f74c611","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"c42c7b14-45d4-42cc-9f36-138fc68b3efb"},"d8012a7d-c563-4dbc-900c-9313cf263f49":{"kind":"popup","cta":"NEXT","audio_url":"","options":{"root":{"theme":"light","close_button":true,"prev_button":true,"show_step_number":true,"spot":"hard","hasSpotlight":false,"spotlightColor":"#9939eb","backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":700,"secondaryButton":false,"maxFrameWidth":null},"description":{"value":"

Create Data Cube

Explore a data set by creating a data cube. This consists of a multidimensional data model used to organize and visualize aggregated data.

Click \"NEXT\" to get a basic walkthrough of how to create and configure a data cube in Imply Polaris.

"},"cta":{"bold":true,"fontSize":16,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"secondary":{"value":"CTA","bold":true,"fontSize":16,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"center","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"js":{"value":""},"version":1},"id":"d8012a7d-c563-4dbc-900c-9313cf263f49","title":"","dom_id":"","page_id":"a01501bf-0ca3-475a-bc20-9f7475f73c30","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"352804b7-7c49-4091-85fa-ed0656d539d0"},"38834e30-5365-4eeb-ae27-eda47f5ea709":{"kind":"hotspot","cta":"Next","audio_url":"","options":{"root":{"alignment":"lc","theme":"light","close_button":true,"next_button":true,"prev_button":true,"secondaryButton":false,"show_step_number":true,"hide_arrow":false,"backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":407,"maxFrameWidth":null,"backdropRect":null,"backdropSelector":null,"offset":{"x":87.9335270371548,"y":55.80623851102941},"offsetFromOriginPx":{"x":0,"y":0},"spot":"none","hasSpotlight":false,"spotlightColor":"#9939eb","trigger":null,"auto_hide_text":true,"beaconColor":"#9939EB","beaconOpacity":1,"zoom":null},"description":{"value":"

Click \"Data cubes\" to view existing data cubes.

","color":"#F3F5F7"},"cta":{"bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#F3F5F7","position":"right","backgroundColor":{"alpha":100,"hex":"#2777dd"},"borderColor":""},"secondary":{"value":"CTA","bold":true,"fontSize":14,"italic":false,"textUnderline":false,"color":"#ffffff","position":"right","backgroundColor":{"alpha":100,"hex":"#9939eb"},"borderColor":""},"meta":{"elementText":"","elementType":""},"version":1,"title":{"color":"#F3F5F7"}},"id":"38834e30-5365-4eeb-ae27-eda47f5ea709","title":"","dom_id":"a[href=\"https://imply.app.imply.io/60b601fa-1eff-4eb0-ba32-65771bf0f527/data-cubes\"]","page_id":"a01501bf-0ca3-475a-bc20-9f7475f73c30","subtitle":"","image_url":"","target_id":null,"video_url":"","stream_url":null,"description":"","target_type":null,"stream_status":null,"thumbnail_url":null,"media_duration":null,"flowId":"352804b7-7c49-4091-85fa-ed0656d539d0"},"8ee4845f-3e18-4340-8f28-3070f5ada99e":{"kind":"hotspot","cta":"Next","audio_url":"","options":{"root":{"alignment":"tr","theme":"light","close_button":true,"next_button":true,"prev_button":true,"secondaryButton":false,"show_step_number":true,"hide_arrow":false,"backgroundColor":"#2C2843","borderColor":"#807f93","frameWidth":null,"maxFrameWidth":null,"backdropRect":null,"backdropSelector":null,"offset":{"x":90.61379509736587,"y":97.99888134002686},"offsetFromOriginPx":{"x":0,"y":0},"spot":"none","hasSpotlight":false,"spotlightColor":"#9939eb","trigger":null,"auto_hide_text":true,"beaconColor":"#9939EB","beaconOpacity":1,"zoom":null},"description":{"value":"

Click \"Create data cube\"

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Creating a data cube is a simple point and click process.

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Choose from the following options

From table: Create a data cube from an existing table.
From SQL query: Create a data cube by writing a query that selects data used to populate the data cube. You can add queries against any data source.

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Click \"From table\"

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Select \"IoT Thermastat Data\" from the drown down menu.

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Click \"Next: Create data cube\"

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The default visualization is the Overall visualization. It presents a numeric summary of the currently selected measures, applying any selected filter criteria.

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Thank you for exploring how to create a Data Cube

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Create Visualizations

Choose from a variety of dynamic visualizations for fast, interactive data exploration. These visualizations aren't static; you can click on elements to drill down and analyze your data further.

Click \"NEXT\" to get a basic walkthrough of how to visualize your data.

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Click into \"Overall\" to explore the different visualization options.

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Scroll down and click \"Time series.\"

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\"Time series\" is used for analyzing data over time, finding patterns and trends, estimating missing values, and adding extra time periods to complete the data.

Let's explore the humidity levels for each thermostat device over time.

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Let's examine a weeks worth of data to identify humidity trends for the three thermostat devices in this data set:

Click into \"Latest Day\" and we will choose \"7 Days\" from the drop down menu for you.

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We will drag the Humidity and Device dimensions into the Timeseries column and Group By fields for you.

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This interactive visualization highlights a common challenge when analyzing IoT devices - incomplete data. Often times, sensors for IoT devices can fail or provide inaccurate readings, leading to erroneous data.

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Let's explore Polaris' advanced capabilities for timeseries data to accurately represent the true state of these thermastats.

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Click into \"Pre-bucketing\" and we will select Hour for you.

Pre-bucketing ensures a consistent time series structure, regardless of whether data points are missing. This organization makes it easier to manage and analyze the data, as it eliminates the irregularities introduced by varying time gaps between data points.

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Click into \"Time-weighted average\" and we will select Hour for you.

Time-weighted averages make data look smoother to help identify trends even if some information is missing. They also fill in gaps where data is missing, ensuring consistent results that consider the timing of each piece of information.

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With time-weighted averages, it is much easier to identify humidity trends, even when some data is missing. They organize the data, fill in missing parts, and keep it structured for useful analysis.

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This interactive visualization allows you to inspect data points by hovering your mouse over them.

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Next, let's examine a line chart to further investigate time-dependent patterns, trends, and real-time monitoring.

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Let's first split on time.

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Next, let's isolate Temp and Humidity.

We will drag \"Temp\" and \"Humidity\" over to the \"Show\" input for you.

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Let's analyze this data for each thermostat over an extended time frame, filtering for the previous month, adding the device, and using 5-minute data aggregation to gain better insights into trends

We will change the filter to the previous month, add device, and select a bucket for 5 minutes for you.

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By examining the data over a longer period, we can clearly identify moments when there was a significant drop in humidity and temperature.

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You can easily drill into any time period by double-clicking with your mouse.

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By narrowing your focus to a small time window, you can quickly identify and investigate significant anomalies or outliers in the dataset.

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Add this visualization to a dashboard by clicking the icon above.

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We'll name this dashboard 'IoT Thermostat Dashboard' for you.

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Click \"Create.\"

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Thank you for exploring Imply Polaris

Sign up for Imply Polaris today and receive $500 in free credit!

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