AWS Big Data

MARCH 25, 2024

The result is made available to the application by querying the latest snapshot. The snapshot constantly updates through stream processing; therefore, the up-to-date data is provided in the context of a user prompt to the model. This allows the model to adapt to the latest changes in price and availability.

Data Lake 93

Data Lake Unstructured Data Management Modeling 93

AWS Big Data

JANUARY 17, 2024

The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query. Next, we create an AWS Cloud9 interactive development environment (IDE). The following are some highlighted steps: Run a snapshot query. %%sql Choose Create key pair.

Data Lake 96

Data Lake Snapshot Big Data Data-driven 96

AWS Big Data

OCTOBER 11, 2023

We introduce you to Amazon Managed Service for Apache Flink Studio and get started querying streaming data interactively using Amazon Kinesis Data Streams. Frequent materialized view refreshes on top of constantly changing base tables due to streamed data can lead to snapshot isolation errors.

Management 107

Management Metadata Analytics Dashboards 107

Cloudera

FEBRUARY 22, 2022

In Iceberg, instead of listing O(n) partitions (directory listing at runtime) in a table for query planning, Iceberg performs an O(1) RPC to read the snapshot. It includes a catalog that supports atomic changes to snapshots – this is required to ensure that we know changes to an Iceberg table either succeeded or failed.

Snapshot 105

Snapshot Metadata Cost-Benefit Data Architecture 105

Cloudera

APRIL 3, 2023

Every table change creates an Iceberg snapshot, this helps to resolve concurrency issues and allows readers to scan a stable table state every time. The table metadata is stored next to the data files under a metadata directory, which allows multiple engines to use the same table simultaneously.

Data Warehouse 79

Data Warehouse Snapshot Metadata Cost-Benefit 79

AWS Big Data

FEBRUARY 1, 2023

To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. We used the same AWS Glue jobs to further transform and load the data into the required S3 bucket and a portion of extracted metadata into DynamoDB.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

SEPTEMBER 13, 2023

Amazon Athena is used for interactive querying and AWS Lake Formation is used for access controls. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. It also updates technical metadata in the AWS Glue Data Catalog.

Data Lake 93

Data Lake Data Processing Metadata Snapshot 93

AWS Big Data

FEBRUARY 13, 2023

To do this, we required the following: A reference cluster snapshot – This ensures that we can replay any tests starting from the same state. A set of queries from the production cluster – This set can be reconstructed from the Amazon Redshift logs ( STL_QUERYTEXT ) and enriched by metadata ( STL_QUERY ). Take measurements 18 x DC2.

Snapshot 67

Snapshot Data Warehouse Testing Analytics 67

Cloudera

NOVEMBER 13, 2020

Metadata Caching. If you have ever interacted with Impala in the past you would have encountered the Catalog Cache Service. As Impala’s adoption grew the catalog service started to experience these growing pains, therefore recently we introduced two new features to alleviate the stress, On-demand Metadata and Zero Touch Metadata.

Optimization 142

Optimization Metadata Statistics Cost-Benefit 142

AWS Big Data

JUNE 12, 2023

After the processed data is stored in Amazon S3, we create an AWS Glue crawler to create a Data Catalog table that acts as a metadata layer for the data. The table can be queried using Amazon Athena , a serverless, interactive query service that enables running SQL-like queries on data stored in Amazon S3.

Management 80

Management Metadata Testing Internet of Things 80

AWS Big Data

NOVEMBER 29, 2023

AWS Glue interactive sessions run the SQL statements to create intermediate tables or final tables, views, or materialized views. S3FileIO --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions" The last two lines are added for setting Iceberg configurations on AWS Glue interactive sessions.

Data Lake 106

Data Lake Management Metrics Data Warehouse 106

AWS Big Data

JANUARY 27, 2023

The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. With unified metadata, both data processing and data consuming applications can access the tables using the same metadata. For metadata read/write, Flink has the catalog interface.

Data Lake 62

Data Lake Metadata Business Analysis Data-driven 62

Alation

AUGUST 26, 2021

It includes intelligence about data, or metadata. The earliest DI use cases leveraged metadata — EG, popularity rankings reflecting the most used data — to surface assets most useful to others. Again, metadata is key. Data Intelligence and Metadata. Data intelligence is fueled by metadata.

Metadata 64

Metadata Data Governance Dashboards Software 64