For similar capabilities to Amazon Timestream for LiveAnalytics, consider Amazon Timestream for InfluxDB. It offers simplified data ingestion and single-digit millisecond query response times for real-time analytics. Learn more [here](https://docs.aws.amazon.com//timestream/latest/developerguide/timestream-for-influxdb.html).

# Using the console
<a name="console_timestream"></a>

 You can use the AWS Management Console for Timestream Live Analytics to create, edit, delete, describe, and list databases and tables. You can also use the console to run queries.

**Topics**
+ [

## Tutorial
](#console_timestream.db-w-sample-data)
+ [

## Create a database
](#console_timestream.db.using-console)
+ [

## Create a table
](#console_timestream.table.using-console)
+ [

## Run a query
](#console_timestream.queries.using-console)
+ [

## Create a scheduled query
](#console_timestream.scheduledquery.using-console)
+ [

## Delete a scheduled query
](#console_timestream.scheduledquerydeletedisable.using-console)
+ [

## Delete a table
](#console_timestream.delete-table.using-console)
+ [

## Delete a database
](#console_timestream.delete-db.using-console)
+ [

## Edit a table
](#console_timestream.edit-table.using-console)
+ [

## Edit a database
](#console_timestream.edit-db.using-console)

## Tutorial
<a name="console_timestream.db-w-sample-data"></a>

 This tutorial shows you how to create a database populated with sample data sets and run sample queries. The sample datasets used in this tutorial are frequently seen in IoT and DevOps scenarios. The IoT dataset contains time series data such as the speed, location, and load of a truck, to streamline fleet management and identify optimization opportunities. The DevOps dataset contains EC2 instance metrics such as CPU, network, and memory utilization to improve application performance and availability. Here's a [video tutorial](https://www.youtube.com/watch?v=YBWCGDd4ChQ) for the instructions described in this section 

Follow these steps to create a database populated with the sample data sets and run sample queries using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Databases**

1. Click on **Create database**.

1. On the create database page, enter the following:
   + **Choose configuration**—Select **Sample database**.
   + **Name**—Enter a database name of your choice.
   + **Choose sample datasets**—Select **IoT** and **DevOps**.
   +  Click on **Create database** to create a database containing two tables—IoT and DevOps populated with sample data. 

1. In the navigation pane, choose **Query editor**

1. Select **Sample queries** from the top menu.

1. Click on one of the sample queries. This will take you back to the query editor with the editor populated with the sample query.

1. Click **Run** to run the query and see query results.

## Create a database
<a name="console_timestream.db.using-console"></a>

Follow these steps to create a database using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Databases**

1. Click on **Create database**.

1. On the create database page, enter the following.
   + **Choose configuration**—Select **Standard database**.
   + **Name**—Enter a database name of your choice.
   + **Encryption **—Choose a KMS key or use the default option, where Timestream Live Analytics will create a KMS key in your account if one does not already exist.

1.  Click on **Create database** to create a database.

## Create a table
<a name="console_timestream.table.using-console"></a>

Follow these steps to create a table using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Tables**

1. Click on **Create table**.

1. On the create table page, enter the following.
   + **Database name**—Select the name of the database created in [Create a database](#console_timestream.db.using-console).
   + **Table name**—Enter a table name of your choice.
   + **Memory store retention**—Specify how long you want to retain data in the memory store. The memory store processes incoming data, including late arriving data (data with a timestamp earlier than the current time) and is optimized for fast point-in-time queries.
   + **Magnetic store retention**—Specify how long you want to retain data in the magnetic store. The magnetic store is meant for long term storage and is optimized for fast analytical queries.

1.  Click on **Create table**.

## Run a query
<a name="console_timestream.queries.using-console"></a>

Follow these steps to run queries using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Query editor**

1. In the left pane, select the database created in [Create a database](#console_timestream.db.using-console).

1. In the left pane, select the database created in [Create a table](#console_timestream.table.using-console).

1. In the query editor, you can run a query. To see the latest 10 rows in the table, run: 

   ```
   SELECT * FROM <database_name>.<table_name> ORDER BY time DESC LIMIT 10
   ```

1. (Optional) Turn on **Enable Insights** to get insights about the efficiency of your queries. 

## Create a scheduled query
<a name="console_timestream.scheduledquery.using-console"></a>

Follow these steps to create a scheduled query using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Scheduled queries**.

1. Click on **Create scheduled query**.

1. In the **Query Name** and **Destination Table** sections, enter the following.
   + **Name**—Enter a query name.
   + **Database name**—Select the name of the database created in [Create a database](#console_timestream.db.using-console).
   + **Table name**—Select the name of the table created in [Create a table](#console_timestream.table.using-console).

1. In the **Query Statement** section, enter a valid query statement. Then click **Validate query**.

1. From **Destination table model**, define the model for any undefined attributes. You can use **Visual builder** or JSON.

1. In the **Run schedule** section, choose **Fixed rate** or **Chron expression**.For chron expressions, refer to [Schedule Expressions for Scheduled Queries](https://docs.aws.amazon.com/timestream/latest/developerguide/scheduledqueries-schedule.html) for more details on schedule expressions. 

1. In the **SNS topic** section, enter the SNS topic that will be used to for notification.

1. In the **Error log report** section enter the S3 location that will be used to report errors.

   Choose the **Encryption key type**.

1. In the **Security settings** section from **AWS KMS key**, choose the type of AWS KMS key.

   Enter the **IAM role** that Timestream for LiveAnalytics will use to run the scheduled query. Refer to the [IAM policy examples for scheduled queries](https://docs.aws.amazon.com/timestream/latest/developerguide/security_iam_id-based-policy-examples.html#security_iam_id-based-policy-examples-sheduledqueries) for details on the required permissions and trust relationship for the role.

1.  Click **Create scheduled query**.

## Delete a scheduled query
<a name="console_timestream.scheduledquerydeletedisable.using-console"></a>

Follow these steps to delete or disable a scheduled query using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Scheduled queries**

1. Select the scheduled query created in [Create a scheduled query](#console_timestream.scheduledquery.using-console).

1. Select **Actions**.

1. Choose **Disable** or **Delete**.

1. If you selected Delete, confirm the action and select **Delete**.

## Delete a table
<a name="console_timestream.delete-table.using-console"></a>

Follow these steps to delete a database using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Tables**

1. Select the table that you created in [Create a table](#console_timestream.table.using-console).

1. Click **Delete**.

1. Type *delete* in the confirmation box.

## Delete a database
<a name="console_timestream.delete-db.using-console"></a>

Follow these steps to delete a database using the AWS Console: 

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Databases**

1. Select the database that you created in **Create a database**.

1. Click **Delete**.

1. Type *delete* in the confirmation box.

## Edit a table
<a name="console_timestream.edit-table.using-console"></a>

Follow these steps to edit a table using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Tables**

1. Select the table that you created in [Create a table](#console_timestream.table.using-console).

1. Click **Edit**

1. Edit the table details and save.
   + **Memory store retention**—Specify how long you want to retain data in the memory store. The memory store processes incoming data, including late arriving data (data with a timestamp earlier than the current time) and is optimized for fast point-in-time queries.
   + **Magnetic store retention**—Specify how long you want to retain data in the magnetic store. The magnetic store is meant for long term storage and is optimized for fast analytical queries.

## Edit a database
<a name="console_timestream.edit-db.using-console"></a>

Follow these steps to edit a database using the AWS Console.

1. Open the [AWS Console](https://console.aws.amazon.com/timestream).

1. In the navigation pane, choose **Databases**

1. Select the database that you created in **Create a database**.

1. Click **Edit**

1. Edit the database details and save.