AWS Big Data

MARCH 2, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

Data Lake 136

Data Lake Data Processing Metadata Snapshot 136

AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

Data Lake 136

Data Lake Data Warehouse Visualization Snapshot 136

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 130

Data Lake Metadata Statistics Optimization 130

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. For more information, refer to Retry Amazon S3 requests with EMRFS. availability.

Data Lake 98

Data Lake Snapshot Metadata Optimization 98

AWS Big Data

SEPTEMBER 13, 2023

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. The company wanted the ability to continue processing operational data in the secondary Region in the rare event of primary Region failure.

Data Lake 111

Data Lake Data Processing Metadata Snapshot 111

AWS Big Data

APRIL 27, 2023

Amazon Athena supports the MERGE command on Apache Iceberg tables, which allows you to perform inserts, updates, and deletes in your data lake at scale using familiar SQL statements that are compliant with ACID (Atomic, Consistent, Isolated, Durable).

Data Lake 96

Data Lake Snapshot Optimization Data Transformation 96

AWS Big Data

DECEMBER 4, 2024

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

Data Integration 113

Data Integration Data Lake Statistics Data-driven 113

AWS Big Data

JANUARY 24, 2024

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. For more information, refer to Amazon S3: Allows read and write access to objects in an S3 Bucket.

Snapshot 126

Snapshot Data Lake Metadata Optimization 126

AWS Big Data

MARCH 4, 2024

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. Schema evolution enables adding, deleting, renaming, or modifying columns without needing to rewrite existing data.

Snapshot 132

Snapshot Data Lake Metadata Recreation/Entertainment 132

AWS Big Data

MARCH 28, 2023

As organizations across the globe are modernizing their data platforms with data lakes on Amazon Simple Storage Service (Amazon S3), handling SCDs in data lakes can be challenging.

Data Lake 98

Data Lake Testing Snapshot Big Data 98

AWS Big Data

SEPTEMBER 12, 2024

Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance. Iceberg creates a new version called a snapshot for every change to the data in the table.

Optimization 128

Optimization Snapshot Metadata Metrics 128

AWS Big Data

OCTOBER 3, 2023

In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) data lake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.

Optimization 126

Optimization Snapshot Data Lake Metadata 126

AWS Big Data

JULY 3, 2023

Major market indexes, such as S&P 500, are subject to periodic inclusions and exclusions for reasons beyond the scope of this post (for an example, refer to CoStar Group, Invitation Homes Set to Join S&P 500; Others to Join S&P 100, S&P MidCap 400, and S&P SmallCap 600 ).

Snapshot 105

Snapshot Data Lake Testing Strategy 105

O'Reilly on Data

OCTOBER 19, 2021

but to reference concrete tooling used today in order to ground what could otherwise be a somewhat abstract exercise. Adapted from the book Effective Data Science Infrastructure. ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. Versioning.

IT 363

IT Testing Experimentation Software 363

AWS Big Data

JUNE 27, 2024

With built-in features such as automated snapshots and cross-Region replication, you can enhance your disaster resilience with Amazon Redshift. Amazon Redshift supports two kinds of snapshots: automatic and manual, which can be used to recover data. Snapshots are point-in-time backups of the Redshift data warehouse.

Snapshot 92

Snapshot Data Warehouse Data Processing Strategy 92

AWS Big Data

JANUARY 17, 2024

With Amazon EMR 6.15, we launched AWS Lake Formation based fine-grained access controls (FGAC) on Open Table Formats (OTFs), including Apache Hudi, Apache Iceberg, and Delta lake. Many large enterprise companies seek to use their transactional data lake to gain insights and improve decision-making.

Data Lake 115

Data Lake Snapshot Big Data Data-driven 115

AWS Big Data

MARCH 27, 2023

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. For Filter by resource type , you can filter by Workgroup , Namespace , Snapshot , and Recovery Point. For more details on tagging, refer to Tagging resources overview. Choose Save changes.

Data Warehouse 98

Data Warehouse Management Snapshot Data Lake 98

AWS Big Data

MAY 15, 2024

By extracting detailed information from CloudTrail and querying it using Athena, this solution streamlines the process of data collection, analysis, and reporting of EIP usage within an AWS account. Additionally, you can analyze activity logs with AWS CloudTrail Lake and Amazon Athena.

Snapshot 115

Snapshot Data Lake Optimization Reporting 115

AWS Big Data

MARCH 25, 2024

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. For more information, refer to Notions of Time: Event Time and Processing Time. For more information, refer to Dynamic Tables.

Data Lake 120

Data Lake Unstructured Data Management Snapshot 120

AWS Big Data

JANUARY 24, 2023

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.

Data Architecture 90

Data Architecture Metadata Data Lake Snapshot 90

AWS Big Data

MAY 30, 2023

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Recently, data lakes have gained lot of traction to become the foundation for analytical solutions, because they come with benefits such as scalability, fault tolerance, and support for structured, semi-structured, and unstructured datasets.

Data Lake 98

Data Lake Data Analytics Analytics Data Processing 98

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 110

Enterprise Data Warehouse Snapshot Cost-Benefit 110

AWS Big Data

NOVEMBER 10, 2023

The data sourcing problem To ensure the reliability of PySpark data pipelines, it’s essential to have consistent record-level data from both dimensional and fact tables stored in the Enterprise Data Warehouse (EDW). These tables are then joined with tables from the Enterprise Data Lake (EDL) at runtime.

Data Processing 111

Data Processing Data Lake Data Warehouse Optimization 111

AWS Big Data

MARCH 23, 2023

Data producer setup In this section, we present the steps to set up the data producer. In the navigation pane, under Register and ingest , choose Data lake locations. For additional information about roles, refer to Requirements for roles used to register locations. The Iceberg table keeps track of the snapshots.

Interactive 93

Interactive Snapshot Data Lake Software 93

AWS Big Data

AUGUST 16, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. With each crawler run, the crawler inspects each of the S3 paths and catalogs the schema information, such as new tables, deletes, and updates to schemas in the Data Catalog.

Data Lake 98

Data Lake Metadata Snapshot Management 98

AWS Big Data

JULY 28, 2023

Tagging Consider tagging your Amazon Redshift resources to quickly identify which clusters and snapshots contain the PII data, the owners, the data retention policy, and so on. Redshift resources, such as namespaces, workgroups, snapshots, and clusters can be tagged. Tags provide metadata about resources at a glance.

Snapshot 98

Snapshot Metadata Measurement Data Warehouse 98

AWS Big Data

JANUARY 8, 2024

For security, Kinesis Data Streams provide server-side encryption so you can meet strict data management requirements by encrypting your data at rest and Amazon Virtual Private Cloud (VPC) interface endpoints to keep traffic between your Amazon VPC and Kinesis Data Streams private.

Analytics 134

Analytics IoT Data-driven Snapshot 134

AWS Big Data

JUNE 21, 2023

When setting out to build a data warehouse, it’s a common pattern to have a data lake as the source of the data warehouse. The data lake in this context serves a number of important functions: It acts as a central source for multiple applications, not just exclusively for data warehousing purposes.

Data Warehouse 104

Data Warehouse Data Lake OLAP Cost-Benefit 104

AWS Big Data

JULY 25, 2023

For more information, refer to Granting access to monitor queries. For a complete list of system views and their uses, refer to Monitoring views. For more information, refer to WLM query monitoring rules. The following screenshot shows the metrics available at the snapshot storage level.

Metrics 98

Metrics Data Warehouse Dashboards Snapshot 98

AWS Big Data

MARCH 18, 2024

Namespaces group together all of the resources you use in Redshift Serverless, such as schemas, tables, users, datashares, and snapshots. To create your namespace and workgroup, refer to Creating a data warehouse with Amazon Redshift Serverless. Workgroup – A collection of compute resources.

Data Warehouse 111

Data Warehouse Visualization Snapshot Data-driven 111

Cloudera

FEBRUARY 14, 2024

Introduction Apache Iceberg has recently grown in popularity because it adds data warehouse-like capabilities to your data lake making it easier to analyze all your data — structured and unstructured. Problem with too many snapshots Everytime a write operation occurs on an Iceberg table, a new snapshot is created.

Optimization 59

Optimization Strategy Snapshot Metadata 59

AWS Big Data

MARCH 3, 2023

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. with Apache Spark version 3.3.0)

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

AWS Big Data

JULY 8, 2024

Refer to Using Apache Flink connectors to stay updated on any future changes regarding connector versions and compatibility. Extending checkpoint intervals allows Apache Flink to prioritize processing throughput over frequent state snapshots, thereby improving efficiency and performance. SQL Apache Flink 1.19 With runtime 1.18

Management 115

Management Dashboards Snapshot Consulting 115

AWS Big Data

AUGUST 2, 2023

The data architecture diagram below shows an example of how you could use AWS services to calculate and visualize an organization’s estimated carbon footprint. Customers have the flexibility to choose the services in each stage of the data pipeline based on their use case.

Data Lake 95

Data Lake Measurement Visualization Data Architecture 95

AWS Big Data

DECEMBER 13, 2023

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. To see how to manage redshift cluster security group, refer Managing VPC security groups for a cluster.

Data Warehouse 116

Data Warehouse Snapshot Data Processing Internet of Things 116

AWS Big Data

FEBRUARY 1, 2023

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

AWS Big Data

JUNE 12, 2023

This schema serves as a single source of truth for producer and consumer and you can leverage the schema evolution feature of AWS Glue Schema Registry to keep it consistent as the data changes over time. Refer appendix section for more information on this feature. Refer to the first stack’s output.

Management 98

Management Metadata Internet of Things Testing 98