Introducing Apache Iceberg materialized views in AWS Glue Knowledge Catalog

December 10, 2025

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Tons of of 1000’s of shoppers construct synthetic intelligence and machine studying (AI/ML) and analytics functions on AWS, regularly remodeling knowledge by means of a number of levels for improved question efficiency—from uncooked knowledge to processed datasets to closing analytical tables. Knowledge engineers should resolve advanced issues, together with detecting what knowledge has modified in base tables, writing and sustaining transformation logic, scheduling and orchestrating workflows throughout dependencies, provisioning and managing compute infrastructure, and troubleshooting failures whereas monitoring pipeline well being. Think about an ecommerce firm the place knowledge engineers must constantly merge clickstream logs with orders knowledge for analytics. Every transformation requires constructing sturdy change detection mechanisms, writing advanced joins and aggregations, coordinating a number of workflow steps, scaling compute assets appropriately, and sustaining operational oversight—all whereas supporting knowledge high quality and pipeline reliability. This complexity calls for months of devoted engineering effort and ongoing upkeep, making knowledge transformation expensive and time-intensive for organizations looking for to unlock insights from their knowledge.

To handle these challenges, AWS introduced a brand new materialized view functionality for Apache Iceberg tables within the AWS Glue Knowledge Catalog. The brand new materialized view functionality simplifies knowledge pipelines and accelerates knowledge lake question efficiency. A materialized view is a managed desk within the AWS Glue Knowledge Catalog that shops pre-computed outcomes of a question in Iceberg format that’s incrementally up to date to mirror modifications to the underlying datasets. This alleviates the necessity to construct and keep advanced knowledge pipelines to generate reworked datasets and speed up question efficiency. Apache Spark engines throughout Amazon Athena, Amazon EMR, and AWS Glue assist the brand new materialized views and intelligently rewrite queries to make use of materialized views that pace up efficiency whereas decreasing compute prices.

On this submit, we present you the way Iceberg materialized view works and learn how to get began.

How Iceberg materialized views work

Iceberg materialized views provide a easy, managed resolution constructed on acquainted SQL syntax. As a substitute of constructing advanced pipelines, you may create materialized views utilizing commonplace SQL queries from Spark, remodeling knowledge with aggregates, filters, and joins with out writing customized knowledge pipelines. Change detection, incremental updates, and monitoring supply tables are robotically dealt with within the AWS Glue Knowledge Catalog and refreshing materialized views as new knowledge arrive, assuaging the necessity for guide pipeline orchestration. Knowledge transformations run on absolutely managed compute infrastructure, eradicating the burden of provisioning, scaling, or sustaining servers.

The ensuing pre-computed knowledge is saved as Iceberg tables in an Amazon Easy Storage Service (Amazon S3) normal function bucket, or Amazon S3 Tables buckets inside the your account, making reworked knowledge instantly accessible to a number of question engines, together with Athena, Amazon Redshift, and AWS optimized Spark runtime. Spark engines throughout Athena, Amazon EMR, and AWS Glue assist an automated question rewrite performance that intelligently makes use of materialized views, delivering automated efficiency enchancment for knowledge processing jobs or interactive pocket book queries.

Within the following sections, we stroll by means of the steps to create, question, and refresh materialized views.

Pre-requisite

To comply with together with this submit, you should have an AWS account.

To run the instruction on Amazon EMR, full the next steps to configure the cluster:

Launch an Amazon EMR cluster 7.12.0 or increased.

SSH login to the first node of your Amazon EMR cluster, and run the next command to begin a Spark software with required configurations:

spark-sql 
  --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions 
  --conf spark.sql.catalog.glue_catalog=org.apache.iceberg.spark.SparkCatalog 
  --conf spark.sql.catalog.glue_catalog.sort=glue 
  --conf spark.sql.catalog.glue_catalog.warehouse=s3://amzn-s3-demo-bucket/warehouse 
  --conf spark.sql.catalog.glue_catalog.glue.area=us-east-1 
  --conf spark.sql.catalog.glue_catalog.glue.id=123456789012 
  --conf spark.sql.catalog.glue_catalog.glue.account-id=123456789012 
  --conf spark.sql.catalog.glue_catalog.consumer.area=us-east-1 
  --conf spark.sql.catalog.glue_catalog.glue.lakeformation-enabled=true 
  --conf spark.sql.optimizer.answerQueriesWithMVs.enabled=true 
  --conf spark.sql.defaultCatalog=glue_catalog

To run the instruction on AWS Glue for Spark, full the next steps to configure the job:

Create an AWS Glue model 5.1 job or increased.
Configure a job parameter
1. Key: --conf
2. Worth: spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions

Configure your job with the next script:

from pyspark.sql import SparkSession


spark = (
    SparkSession.builder 
        .config("spark.sql.extensions", "org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions")
        .config("spark.sql.catalog.glue_catalog", "org.apache.iceberg.spark.SparkCatalog")
        .config("spark.sql.catalog.glue_catalog.sort", "glue")
        .config("spark.sql.catalog.glue_catalog.warehouse", "s3://amzn- -demo-bucket/warehouse")
        .config("spark.sql.catalog.glue_catalog.glue.area", "us-east-1")
        .config("spark.sql.catalog.glue_catalog.glue.id", "123456789012")
        .config("spark.sql.catalog.glue_catalog.glue.account-id", "123456789012")
		.config("spark.sql.catalog.glue_catalog.consumer.area", "us-east-1")
        .config("spark.sql.catalog.glue_catalog.glue.lakeformation-enabled", "true")
        .config("spark.sql.optimizer.answerQueriesWithMVs.enabled", "true")
        .config("spark.sql.defaultCatalog", "glue_catalog")
        .getOrCreate()
)

Run the next queries utilizing Spark SQL to arrange a base desk. In AWS Glue, you may run them by means of spark.sql("QUERY STATEMENT").

CREATE DATABASE IF NOT EXIST iceberg_mv;

USE iceberg_mv;

CREATE TABLE IF NOT EXISTS base_tbl (
    id INT,
    customer_name STRING,
    quantity INT,
    order_date DATE);
    
INSERT INTO base_tbl VALUES (1, 'John Doe', 150, DATE('2025-12-01')), (2, 'Jane Smith', 200, DATE('2025-12-02')), (3, 'Bob Johnson', 75, DATE('2025-12-03'));

SELECT * FROM base_tbl;

Within the subsequent sections, we create a materialized view with this base desk.

If you wish to retailer your materialized views in Amazon S3 Tables as an alternative of a normal Amazon S3 bucket, seek advice from Appendix 1 on the finish of this submit for the configuration particulars.

Create a materialized view

To create a materialized view, run the next command:

CREATE MATERIALIZED VIEW mv
AS SELECT
    customer_name, 
    COUNT(*) as mv_order_count, 
    SUM(quantity) as mv_total_amount 
FROM glue_catalog.iceberg_mv.base_tbl
GROUP BY customer_name;

After you create a materialized view, AWS Spark’s in-memory metadata cache wants time to populate with details about the brand new materialized view. Throughout this cache inhabitants interval, queries towards the bottom desk will run usually with out utilizing the materialized view. After the cache is absolutely populated (usually inside tens of seconds), Spark robotically detects that the materialized view can fulfill the question and rewrites it to make use of the pre-computed materialized view as an alternative, bettering efficiency.

To see this habits, run the next EXPLAIN command instantly after creating the materialized view:

EXPLAIN EXTENDED
SELECT customer_name, COUNT(*) as mv_order_count, SUM(quantity) as mv_total_amount 
FROM base_tbl
GROUP BY customer_name;

The next output reveals the preliminary end result earlier than cache inhabitants:

== Parsed Logical Plan ==
'Combination ['customer_name], ['customer_name, 'COUNT(1) AS mv_order_count#0, 'SUM('amount) AS mv_total_amount#1]
+- 'UnresolvedRelation [base_tbl] , [], false

== Analyzed Logical Plan ==
customer_name: string, mv_order_count: bigint, mv_total_amount: bigint
Combination [customer_name#8], [customer_name#8, count(1) AS mv_order_count#0L, sum(amount#9) AS mv_total_amount#1L]
+- SubqueryAlias glue_catalog.iceberg_mv.base_tbl
   +- RelationV2[id#7, customer_name#8, amount#9, order_date#10] glue_catalog.iceberg_mv.base_tbl glue_catalog.iceberg_mv.base_tbl

== Optimized Logical Plan ==
Combination [customer_name#8], [customer_name#8, count(1) AS mv_order_count#0L, sum(amount#9) AS mv_total_amount#1L]
+- RelationV2[customer_name#8, amount#9] glue_catalog.iceberg_mv.base_tbl

== Bodily Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[customer_name#8], capabilities=[count(1), sum(amount#9)], output=[customer_name#8, mv_order_count#0L, mv_total_amount#1L], schema specialised)
   +- Change hashpartitioning(customer_name#8, 1000), ENSURE_REQUIREMENTS, [plan_id=19]
      +- HashAggregate(keys=[customer_name#8], capabilities=[partial_count(1), partial_sum(amount#9)], output=[customer_name#8, count#27L, sum#29L], schema specialised)
         +- BatchScan glue_catalog.iceberg_mv.base_tbl[customer_name#8, amount#9] glue_catalog.iceberg_mv.base_tbl (department=null) [filters=, groupedBy=, pushedLimit=None] RuntimeFilters: []

On this preliminary execution plan, Spark scans the base_tbl straight (BatchScan glue_catalog.iceberg_mv.base_tbl) and runs aggregations (COUNT and SUM) on the uncooked knowledge. That is the habits earlier than the materialized view metadata cache is populated.

After ready roughly tens of seconds for the metadata cache inhabitants, run the identical EXPLAIN command once more. The next output reveals the first variations within the question optimization plan after cache inhabitants:

== Optimized Logical Plan ==
Combination [customer_name#97], [customer_name#97, coalesce(sum(mv_order_count#98L), 0) AS mv_order_count#72L, sum(mv_total_amount#99L) AS mv_total_amount#73L]
+- RelationV2[customer_name#97, mv_order_count#98L, mv_total_amount#99L] glue_catalog.iceberg_mv.mv

== Bodily  Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- HashAggregate(keys=[customer_name#97], capabilities=[sum(mv_order_count#98L), sum(mv_total_amount#99L)], output=[customer_name#97, mv_order_count#72L, mv_total_amount#73L], schema specialised)
   +- Change hashpartitioning(customer_name#97, 1000), ENSURE_REQUIREMENTS, [plan_id=51]
      +- HashAggregate(keys=[customer_name#97], capabilities=[partial_sum(mv_order_count#98L), partial_sum(mv_total_amount#99L)], output=[customer_name#97, sum#113L, sum#115L], schema specialised)
         +- BatchScan glue_catalog.iceberg_mv.mv[customer_name#97, mv_order_count#98L, mv_total_amount#99L] glue_catalog.iceberg_mv.mv (department=null) [filters=, groupedBy=, pushedLimit=None] RuntimeFilters: []

After the cache is populated, Spark now scans the materialized view (BatchScan glue_catalog.iceberg_mv.mv) as an alternative of the bottom desk. The question has been robotically rewritten to learn from the pre-computed aggregated knowledge within the materialized view. The output particularly reveals the aggregation capabilities now merely sum the pre-computed values (sum(mv_order_count) and sum(mv_total_amount)) fairly than recalculating COUNT and SUM from uncooked knowledge.

Create a materialized view with scheduling automated refresh

By default, a newly created materialized view accommodates the preliminary question outcomes. It’s not robotically up to date when the underlying base desk knowledge modifications. To maintain your materialized view synchronized with the bottom desk knowledge, you may configure automated refresh schedules. To allow automated refresh, use the REFRESH EVERY clause when creating the materialized view. This clause accepts a time interval and unit, so you may specify how regularly the materialized view is up to date.

The next instance creates a materialized view that robotically refreshes each 24 hours:

CREATE MATERIALIZED VIEW mv
REFRESH EVERY 24 HOURS
AS SELECT
    customer_name, 
    COUNT(*) as mv_order_count, 
    SUM(quantity) as mv_total_amount 
FROM glue_catalog.iceberg_mv.base_tbl
GROUP BY customer_name;

You may configure the refresh interval utilizing any of the next time models: SECONDS, MINUTES, HOURS, or DAYS. Select an applicable interval primarily based in your knowledge freshness necessities and question patterns.

If you happen to want extra management over when your materialized view updates, or must refresh it outdoors of the scheduled intervals, you may set off guide refreshes at any time. We offer detailed directions on guide refresh choices, together with full and incremental refresh, later on this submit.

Question a materialized view

To question a materialized view in your Amazon EMR cluster and retrieve its aggregated knowledge, you should utilize an ordinary SELECT assertion:

This question retrieves all rows from the materialized view. The output reveals the aggregated buyer order counts and complete quantities. The end result shows three prospects with their respective metrics:

-- Consequence
Jane Smith    1    200
Bob Johnson    1    75
John Doe    1    150

Moreover, you may question the identical materialized view from Athena SQL. The next screenshot reveals the identical question run on Athena and the ensuing output.

Refresh a materialized view

You may refresh materialized views utilizing two refresh varieties: full refresh or incremental refresh. Full refresh re-computes the whole materialized view from all base desk knowledge. Incremental refresh processes solely the modifications because the final refresh. Full refresh is good once you want consistency or after vital knowledge modifications. Incremental refresh is most well-liked once you want rapid updates. The next examples present each refresh varieties.

To make use of full refresh, full the next steps:

Insert three new data into the bottom desk to simulate new knowledge arriving:

INSERT INTO base_tbl VALUES 
(4, 'Jane Smith', 350, DATE('2025-11-29')), 
(5, 'Bob Johnson', 100, DATE('2025-11-30')), 
(6, 'Kwaku Mensah', 40, DATE('2025-12-01'));

Question the materialized view to confirm it nonetheless reveals the previous aggregated values:

SELECT * FROM mv;

-- Consequence
Jane Smith    1    200
Bob Johnson    1    75
John Doe    1    150

Run a full refresh of the materialized view utilizing the next command:
```
REFRESH MATERIALIZED VIEW mv FULL;
```

Question the materialized view once more to confirm the aggregated values now embrace the brand new data:

SELECT * FROM mv;

-- Consequence
Jane Smith    2    550 // Up to date
Bob Johnson    2    175  // Up to date
John Doe    1    150
Kwaku Mensah    1    40 // Added

To make use of incremental refresh, full the next steps:

Allow incremental refresh by setting the Spark configuration properties:
```
SET spark.sql.optimizer.incrementalMVRefresh.enabled=true;
```

Insert two further data into the bottom desk:

INSERT INTO base_tbl VALUES 
(7, 'Jane Smith', 120, DATE('2025-11-28')), 
(8, 'Kwaku Mensah', 90, DATE('2025-12-02'));

Run an incremental refresh utilizing the REFRESH command with out the FULL clause. To confirm if incremental refresh is enabled, seek advice from Appendix 2 on the finish of this submit.
```
REFRESH MATERIALIZED VIEW mv;
```

Question the materialized view to substantiate the incremental modifications are mirrored within the aggregated outcomes:

SELECT * FROM mv;

--Consequence
Jane Smith    3    670    3    3 // Up to date
Bob Johnson    2    175    2    2 
John Doe    1    150    1    1
Kwaku Mensah    2    130    2    2 // Up to date

Along with utilizing Spark SQL, you can even set off guide refreshes by means of AWS Glue APIs once you want updates outdoors your scheduled intervals. Run the next AWS CLI command:

$ aws glue start-materialized-view-refresh-task-run 
    --catalog-id  
    --database-name  
    --table-name

The AWS Lake Formation console shows refresh historical past for API-triggered updates. Open your materialized view to see the refresh sort (INCREMENTAL or FULL), begin and finish time, standing and so forth:

You may have realized learn how to use Iceberg materialized views to make your environment friendly knowledge processing and queries. You created a materialized view utilizing Spark on Amazon EMR, queried it from each Amazon EMR and Athena, and used two refresh mechanisms: full refresh and incremental refresh. Iceberg materialized views aid you remodel and optimize your knowledge pipelines effortlessly.

Issues

There are essential points to contemplate for optimum utilization of the potential:

We launched new SQL syntax to handle materialized views within the AWS optimized Spark runtime engine solely. These new SQL instructions can be found in Spark model 3.5.6 and above throughout Athena, Amazon EMR, and AWS Glue. Open supply Spark is just not supported.
Materialized views are ultimately in keeping with base tables. When supply tables change, the materialized views are up to date by means of background refresh processes as outlined by customers within the refresh schedule at creation. Throughout the refresh window, queries straight accessing materialized views would possibly see outdated knowledge. Nonetheless, prospects who want rapid entry to probably the most up-to-date datasets can run a guide refresh with a easy REFRESH MATERIALIZED VIEW SQL command.

Clear up

To keep away from incurring future prices, clear up the assets you created throughout this walkthrough:

Run the next instructions to delete a materialized view and tables:

DROP TABLE mv PURGE;
-- Or, DROP MATERIALIZED VIEW mv;

DROP TABLE base_tbl PURGE;
-- If essential, delete the database by DROP DATABASE iceberg_mv;

For Amazon EMR, terminate the Amazon EMR cluster.
For AWS Glue, delete the AWS Glue job.

Conclusion

This submit demonstrated how Iceberg materialized views facilitate environment friendly knowledge lake operations on AWS. The brand new materialized view functionality simplifies knowledge pipelines and improves question efficiency by storing pre-computed outcomes which can be robotically up to date as base tables change. You may create materialized views utilizing acquainted SQL syntax, utilizing each full and incremental refresh mechanisms to keep up knowledge consistency. This resolution alleviates the necessity for advanced pipeline upkeep whereas offering seamless integration with AWS companies like Athena, Amazon EMR, and AWS Glue. The automated question rewrite performance additional optimizes efficiency by intelligently using materialized views when relevant, making it a robust instrument for organizations seeking to streamline their knowledge transformation workflows and speed up question efficiency.

Appendix 1: Spark configuration to make use of Amazon S3 Tables storing Apache Iceberg materialized views

As talked about earlier on this submit, materialized views are saved as Iceberg tables in Amazon S3 Tables buckets inside your account. While you wish to use Amazon S3 Tables because the storage location to your materialized views as an alternative of a normal Amazon S3 bucket, you should configure Spark with the Amazon S3 Tables catalog.

The distinction from the usual AWS Glue Knowledge Catalog configuration proven within the stipulations part is the glue.id parameter format. For Amazon S3 Tables, use the format :s3tablescatalog/ as an alternative of simply the account ID:

spark-sql 
  --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions 
  --conf spark.sql.catalog.s3t_catalog=org.apache.iceberg.spark.SparkCatalog 
  --conf spark.sql.catalog.s3t_catalog.sort=glue 
  --conf spark.sql.catalog.s3t_catalog.warehouse="s3://amzn-s3-demo-bucket/warehouse" 
  --conf spark.sql.catalog.s3t_catalog.glue.area="us-east-1" 
  --conf spark.sql.catalog.s3t_catalog.glue.id="123456789012:s3tablescatalog/amzn-s3-demo-table-bucket" 
  --conf spark.sql.catalog.s3t_catalog.glue.account-id=123456789012 
  --conf spark.sql.catalog.s3t_catalog.consumer.area="us-east-1" 
  --conf spark.sql.catalog.s3t_catalog.glue.lakeformation-enabled=true 
  --conf spark.sql.optimizer.answerQueriesWithMVs.enabled=true 
  --conf spark.sql.defaultCatalog=s3t_catalog

After you configure Spark with these settings, you may create and handle materialized views utilizing the identical SQL instructions proven on this submit, and the materialized views are saved in your Amazon S3 Tables bucket.

Appendix 2: Confirm refreshing a materialized view with Spark SQL

Run SHOW TBLPROPERTIES in Spark SQL to test which refresh methodology was used:

+-------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
|key                            |worth                                                                                                                             |
+-------------------------------+----------------------------------------------------------------------------------------------------------------------------------+
|IMV_ansiEnabled                |false                                                                                                                             |
|IMV_catalogInfo                |[{"catalogId":"123456789012","catalogName":"glue_catalog"}]                                                                       |
|IMV_mvCatalogID                |123456789012                                                                                                                      |
|IMV_mvNamespace                |iceberg_mv                                                                                                                        |
|IMV_region                     |us-east-1                                                                                                                         |
|IMV_sparkVersion               |3.5.6-amzn-1                                                                                                                      |
|current-snapshot-id            |5750703934418352571                                                                                                               |
|format                         |iceberg/parquet                                                                                                                   |
|format-version                 |2                                                                                                                                 |
|isMaterializedView             |true                                                                                                                              |
|lastRefreshType                |INCREMENTAL                                                                                                                       |
|subObjects                     |[{"Version":"4887707562550190856","DatabaseName":"iceberg_mv","Region":"us-east-1","CatalogId":"123456789012","Name":"base_tbl"}] |
|tableVersionToken              |*********(redacted)                                                                                                               |
|viewOriginalText               |SELECTncustomer_name, nCOUNT(*) as mv_order_count, nSUM(quantity) as mv_total_amount nFROM base_tblnGROUP BY customer_name     |
|viewVersionId                  |5750703934418352571                                                                                                               |
|viewVersionToken               |*********(redacted)                                                                                                               |
|write.parquet.compression-codec|zstd                                                                                                                              |
+-------------------------------+----------------------------------------------------------------------------------------------------------------------------------+