- Release Notes
- Get Started
- Clusters
- Cloud Settings
- Table Type
- Query Data Lakes
- Integration
- Query Acceleration
- Data Loading
- Concepts
- Batch load data from Amazon S3
- Batch load data from Azure cloud storage
- Load data from a local file system
- Load data from Confluent Cloud
- Load data from Amazon MSK
- Load data from Amazon Kinesis
- Data Unloading
- Data Backup
- Security
- Console Access Control
- Data Access Control
- Application keys
- Service accounts
- Use SSL connection
- Alarm
- Usage and Billing
- Organizations and Accounts
- Reference
- Amazon Web Services (AWS)
- Microsoft Azure
- SQL Reference
- Keywords
- ALL statements
- User Account Management
- Cluster Management
- ADMIN CANCEL REPAIR
- ADMIN CHECK TABLET
- ADMIN REPAIR
- ADMIN SET CONFIG
- ADMIN SET REPLICA STATUS
- ADMIN SHOW CONFIG
- ADMIN SHOW REPLICA DISTRIBUTION
- ADMIN SHOW REPLICA STATUS
- ALTER RESOURCE GROUP
- ALTER SYSTEM
- CANCEL DECOMMISSION
- CREATE FILE
- CREATE RESOURCE GROUP
- DROP FILE
- DROP RESOURCE GROUP
- EXPLAIN
- INSTALL PLUGIN
- SET
- SHOW BACKENDS
- SHOW BROKER
- SHOW COMPUTE NODES
- SHOW FRONTENDS
- SHOW FULL COLUMNS
- SHOW INDEX
- SHOW PLUGINS
- SHOW PROCESSLIST
- SHOW RESOURCE GROUP
- SHOW TABLE STATUS
- SHOW FILE
- SHOW VARIABLES
- UNINSTALL PLUGIN
- DDL
- ALTER DATABASE
- ALTER MATERIALIZED VIEW
- ALTER TABLE
- ALTER VIEW
- ANALYZE TABLE
- BACKUP
- CANCEL ALTER TABLE
- CANCEL BACKUP
- CANCEL RESTORE
- CREATE ANALYZE
- CREATE DATABASE
- CREATE EXTERNAL CATALOG
- CREATE INDEX
- CREATE MATERIALIZED VIEW
- CREATE REPOSITORY
- CREATE TABLE AS SELECT
- CREATE TABLE LIKE
- CREATE TABLE
- CREATE VIEW
- CREATE FUNCTION
- DROP ANALYZE
- DROP STATS
- DROP CATALOG
- DROP DATABASE
- DROP INDEX
- DROP MATERIALIZED VIEW
- DROP REPOSITORY
- DROP TABLE
- DROP VIEW
- DROP FUNCTION
- KILL ANALYZE
- RECOVER
- REFRESH EXTERNAL TABLE
- RESTORE
- SET CATALOG
- SHOW ANALYZE JOB
- SHOW ANALYZE STATUS
- SHOW META
- SHOW FUNCTION
- TRUNCATE TABLE
- USE
- DML
- ALTER LOAD
- ALTER ROUTINE LOAD
- BROKER LOAD
- CANCEL LOAD
- CANCEL EXPORT
- CANCEL REFRESH MATERIALIZED VIEW
- CREATE ROUTINE LOAD
- DELETE
- EXPORT
- GROUP BY
- INSERT
- PAUSE ROUTINE LOAD
- RESUME ROUTINE LOAD
- REFRESH MATERIALIZED VIEW
- SELECT
- SHOW ALTER
- SHOW ALTER MATERIALIZED VIEW
- SHOW BACKUP
- SHOW CATALOGS
- SHOW CREATE CATALOG
- SHOW CREATE MATERIALIZED VIEW
- SHOW CREATE TABLE
- SHOW CREATE VIEW
- SHOW DATA
- SHOW DATABASES
- SHOW DELETE
- SHOW DYNAMIC PARTITION TABLES
- SHOW EXPORT
- SHOW LOAD
- SHOW MATERIALIZED VIEW
- SHOW PARTITIONS
- SHOW REPOSITORIES
- SHOW RESTORE
- SHOW ROUTINE LOAD
- SHOW ROUTINE LOAD TASK
- SHOW SNAPSHOT
- SHOW TABLES
- SHOW TABLET
- SHOW TRANSACTION
- STOP ROUTINE LOAD
- STREAM LOAD
- SUBMIT TASK
- UPDATE
- Auxiliary Commands
- Data Types
- Keywords
- SQL Functions
- Function list
- Java UDFs
- Window functions
- Lambda expression
- Date Functions
- add_months
- adddate
- convert_tz
- current_date
- current_time
- current_timestamp
- date
- date_add
- date_diff
- date_format
- date_slice
- date_sub, subdate
- date_trunc
- datediff
- day
- dayofweek_iso
- dayname
- dayofmonth
- dayofweek
- dayofyear
- days_add
- days_diff
- days_sub
- from_days
- from_unixtime
- hour
- hours_add
- hours_diff
- hours_sub
- jodatime_format
- last_day
- makedate
- microseconds_add
- microseconds_sub
- minute
- minutes_add
- minutes_diff
- minutes_sub
- month
- monthname
- months_add
- months_diff
- months_sub
- next_day
- now
- previous_day
- quarter
- second
- seconds_add
- seconds_diff
- seconds_sub
- str_to_date
- str_to_jodatime
- str2date
- time_slice
- time_to_sec
- timediff
- timestamp
- timestampadd
- timestampdiff
- to_date
- to_days
- to_iso8601
- to_tera_date
- to_tera_timestamp
- unix_timestamp
- utc_timestamp
- week
- week_iso
- weekofyear
- weeks_add
- weeks_diff
- weeks_sub
- year
- years_add
- years_diff
- years_sub
- Aggregate Functions
- any_value
- approx_count_distinct
- array_agg
- avg
- bitmap
- bitmap_agg
- count
- count_if
- corr
- covar_pop
- covar_samp
- group_concat
- grouping
- grouping_id
- hll_empty
- hll_hash
- hll_raw_agg
- hll_union
- hll_union_agg
- max
- max_by
- min
- min_by
- multi_distinct_sum
- multi_distinct_count
- percentile_approx
- percentile_cont
- percentile_disc
- retention
- stddev
- stddev_samp
- sum
- variance, variance_pop, var_pop
- var_samp
- window_funnel
- Geographic Functions
- String Functions
- append_trailing_char_if_absent
- ascii
- char
- char_length
- character_length
- concat
- concat_ws
- ends_with
- find_in_set
- group_concat
- hex
- hex_decode_binary
- hex_decode_string
- instr
- lcase
- left
- length
- locate
- lower
- lpad
- ltrim
- money_format
- null_or_empty
- parse_url
- repeat
- replace
- reverse
- right
- rpad
- rtrim
- space
- split
- split_part
- substring_index
- starts_with
- strleft
- strright
- str_to_map
- substring
- trim
- ucase
- unhex
- upper
- url_decode
- url_encode
- Pattern Matching Functions
- JSON Functions
- Overview of JSON functions and operators
- JSON operators
- JSON constructor functions
- JSON query and processing functions
- Bit Functions
- Bitmap Functions
- Array Functions
- all_match
- any_match
- array_agg
- array_append
- array_avg
- array_concat
- array_contains
- array_contains_all
- array_cum_sum
- array_difference
- array_distinct
- array_filter
- array_generate
- array_intersect
- array_join
- array_length
- array_map
- array_max
- array_min
- array_position
- array_remove
- array_slice
- array_sort
- array_sortby
- array_sum
- arrays_overlap
- array_to_bitmap
- cardinality
- element_at
- reverse
- unnest
- Map Functions
- Binary Functions
- cast function
- hash function
- Cryptographic Functions
- Math Functions
- Pattern Matching Functions
- Percentile Functions
- Scalar Functions
- Struct Functions
- Table Functions
- Utility Functions
- AUTO_INCREMENT
- Generated columns
- System variables
- System limits
- Information Schema
- Overview
- be_bvars
- be_cloud_native_compactions
- be_compactions
- character_sets
- collations
- column_privileges
- columns
- engines
- events
- global_variables
- key_column_usage
- load_tracking_logs
- loads
- materialized_views
- partitions
- pipe_files
- pipes
- referential_constraints
- routines
- schema_privileges
- schemata
- session_variables
- statistics
- table_constraints
- table_privileges
- tables
- tables_config
- task_runs
- tasks
- triggers
- user_privileges
- views
- System Metadatabase
- API
- Overview
- Actions
- Clusters
- Create and Manage Clusters
- Query Clusters
- Identity and Access Management
- Organization and Account
- Usage and Billing
- Clusters
- Terraform Provider
- Run scripts
Manage data credentials for AWS
A data credential for AWS in CelerData declares read and write permissions on an S3 bucket, which is used to store query profiles. A query profile is used to visualize the details of a query execution. The query profile helps you troubleshoot performance bottlenecks during the query's execution. Note that the bucket that you decide to use must reside in the AWS region in which you want to deploy CelerData clusters. If you do not have such a bucket, create one before you start your journey with CelerData.
CelerData automatically generates a data credential upon each successful cluster deployment on AWS. You can manage these data credentials for AWS, including creating, viewing, and deleting a data credential.
To ensure a successful cluster deployment in your own VPC, you must have a data credential to select, or create one, during the deployment process.
Create a data credential
The instructions below show you how to create a data credential from the Cloud settings page in the CelerData Cloud BYOC console before you create a deployment. You can also create a data credential in a similar way as part of the workflow of creating a deployment. See Deployment on AWS.
NOTE
If you create a deployment without selecting an existing data credential, CelerData automatically creates a data credential based on your input during deployment and saves it for future use.
To create a data credential before deployment, follow these steps:
Sign in to the CelerData Cloud BYOC console.
In the left-side navigation pane, choose Cloud settings > AWS.
On the Data credentials tab of the AWS Cloud page, click Create data credential.
In the Create data credential dialog box, configure the following parameters and click Submit.
Parameter Required Description Data credential name Yes Enter the name of the data credential.
NOTE
The name must be unique within your CelerData cloud account.Bucket name Yes Enter the name of your bucket.
NOTE
When you create a cluster, you can only use a data credential that references a bucket located in the same region as the cluster.IAM policy information N/A The JSON policy document that you use to create a policy. The policy defines the permissions on your bucket. Instance profile ARN Yes Enter the instance profile ARN of the IAM role that you have created to grant CelerData permission to access your bucket. For Instance profile ARN, you need to follow the instructions provided in Create a service IAM role for EC2 to create a service IAM role in the AWS IAM console and copy the instance profile ARN of the service IAM role.
On the Data credentials tab of the AWS Cloud page, the data credential that you just created is shown.
When you create a cluster, you can select and reuse a data credential that you have already created. After you decide which data credential to use, you need to obtain the ARN of the data credential role and the name of the data credential bucket (for easy understanding, the IAM role and bucket referenced in the data credential are referred to as the data credential role and the data credential bucket) and create a deployment credential to which a policy that contains the data credential's ARN and the data credential bucket's name is attached. See Create a deployment credential and Create a cross-account IAM role.
View a data credential
Before you start a deployment, you can view all of the data credentials created within your CelerData cloud account and find the one that best suits your deployment requirements. Then, you can select that data credential during the deployment process.
To view a data credential, follow these steps:
Sign in to the CelerData Cloud BYOC console.
In the left-side navigation pane, choose Cloud settings > AWS.
On the Data credentials tab of the AWS Cloud page, click the data credential whose details you want to view.
On the right-side pane that appears, view the details about the data credential.
Delete a data credential
Data credentials cannot be edited after they are created. If a data credential has incorrect data or if you no longer need a data credential, you can follow these steps to delete it:
Sign in to the CelerData Cloud BYOC console.
In the left-side navigation pane, choose Cloud Settings > AWS.
On the Data credentials tab of the AWS Cloud page, click the data credential that you want to delete.
On the right-side pane that appears, click Delete.
In the dialog box that appears, enter Delete and click Delete.
NOTE
A data credential cannot be deleted if there are still CelerData clusters created based on it. Therefore, before you delete a data credential, make sure all CelerData clusters that are created by using the data credential are released.
Usage notes
When you are creating a deployment, you cannot edit an existing data credential that you select. If no existing data credentials can meet your deployment requirements, we recommend that you create a new data credential.