AWS Big Data

FEBRUARY 13, 2023

Test environment In order to be confident with the performance of the RA3 nodes, we decided to stress test them in a controlled environment before making the decision to migrate. To do this, we required the following: A reference cluster snapshot – This ensures that we can replay any tests starting from the same state.

Snapshot 65

Snapshot Data Warehouse Testing Analytics 65

AWS Big Data

SEPTEMBER 13, 2023

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started ).

Data Lake 90

Data Lake Data Processing Metadata Snapshot 90

Cloudera

JULY 14, 2023

They also provide a “ snapshot” procedure that creates an Iceberg table with a different name with the same underlying data. You could first create a snapshot table, run sanity checks on the snapshot table, and ensure that everything is in order. Hive creates Iceberg’s metadata files for the same exact table.

Snapshot 69

Snapshot Metadata Data Warehouse Testing 69

AWS Big Data

AUGUST 16, 2023

Change Data Capture (CDC) in the context of a data lake refers to the process of capturing and propagating changes made to source data. On the Code tab, choose Test , then Configure test event. Configure a test event with the default hello-world template event JSON.

Data Lake 69

Data Lake Metadata Testing Snapshot 69

AWS Big Data

MAY 10, 2023

This event is referred to as a zonal failover. However, it’s also possible for multiple shard copies across both active zones to be unavailable in cases of two node failures or one zone plus one node failure (often referred to as double faults ), which poses a risk to availability. We discuss a few of these methods in this section.

Snapshot 92

Snapshot Testing Metadata Management 92

AWS Big Data

APRIL 3, 2024

By selecting the corresponding asset, you can understand its content through the readme, glossary terms , and technical and business metadata. By analyzing the historical report snapshot, you can identify areas for improvement, implement changes, and measure the effectiveness of those changes.

Data Quality 88

Data Quality Visualization Metadata Metrics 88

AWS Big Data

NOVEMBER 16, 2023

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge and adherence to battle-tested best practices, and using the right tools and features in the right scenario. system implemented with Amazon Redshift.

Enterprise 83

Enterprise Data Warehouse Snapshot Cost-Benefit 83

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements. Did it fail?

Testing 130

Testing Data Governance Data Quality Data-driven 130

AWS Big Data

FEBRUARY 1, 2023

Architecturally, we chose a serverless model, and the data lake architecture action line refers to all the architectural gaps and challenging features we determined were part of the improvements. For more details, refer to Connection Types and Options for ETL in AWS Glue. We also used AWS Lambda for data processing.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

JUNE 9, 2023

It is crucial that you perform testing to ensure that a table format meets your specific use case requirements. Refer to Working with other AWS services in the Lake Formation documentation for an overview of table format support when using Lake Formation with other AWS services.

Data Lake 113

Data Lake Metadata Optimization Statistics 113

AWS Big Data

JUNE 12, 2023

Refer appendix section for more information on this feature. 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. Refer to the first stack’s output. Refer to the first stack’s output. Refer to the first stack’s output.

Management 76

Management Metadata Testing Internet of Things 76

AWS Big Data

NOVEMBER 29, 2023

In 2022, AWS published a dbt adapter called dbt-glue —the open source, battle-tested dbt AWS Glue adapter that allows data engineers to use dbt for cloud-based data lakes along with data warehouses and databases, paying for just the compute they need. To learn more, refer to About dbt models. and Viewpoint.

Data Lake 102

Data Lake Management Metrics Data Warehouse 102

Cloudera

JUNE 2, 2021

As an important part of achieving better scalability, Ozone separates the metadata management among different services: . Ozone Manager (OM) service manages the metadata of the namespace such as volume, bucket and keys. Datanode service manages the metadata of blocks, containers and pipelines running on the datanode. .

Metadata 95

Metadata Snapshot Testing Management 95

Cloudera

JULY 13, 2023

A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself. The data files and metadata files in Iceberg format are immutable.

Metadata 69

Metadata Snapshot Data Warehouse Statistics 69

Cloudera

NOVEMBER 13, 2020

For a more in-depth description of these phases please refer to Impala: A Modern, Open-Source SQL Engine for Hadoop. Metadata Caching. In the previous design each Impala coordinator daemon kept an entire copy of the contents of the catalog cache in memory and had to be explicitly notified of any external metadata changes.

Optimization 142

Optimization Metadata Statistics Cost-Benefit 142

Cloudera

MAY 24, 2021

These include workload reviews, testing and validation, managing service-level agreements (SLAs), and minimizing workload unavailability during the move. . Second, configure a replication process to provide periodic and consistent snapshots of data, metadata, and accompanying governance policies. But, Spark 1.6 When to use.

Metadata 80

Metadata Testing Snapshot Strategy 80