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 86

Data Lake Unstructured Data Management Modeling 86

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 81

Data Warehouse Management Snapshot Data Lake 81

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 89

Data Processing Data Lake Data Warehouse Optimization 89

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 80

Enterprise Data Warehouse Snapshot Cost-Benefit 80

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 75

Data Lake Metadata Snapshot Management 75

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 66

Interactive Snapshot Data Lake Software 66

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 109

Analytics IoT Data-driven Snapshot 109

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 87

Data Lake Data Processing Metadata Snapshot 87

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 109

Data Lake Data Warehouse Visualization Snapshot 109

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 94

Data Warehouse Snapshot Data Processing Management 94

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 68

Data Lake Testing Snapshot Sales 68

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 80

Data Lake Snapshot Metadata Optimization 80

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 70

Data Lake Snapshot Optimization Data Transformation 70

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 80

Snapshot Data Lake Testing Strategy 80

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 109

Data Lake Metadata Optimization Statistics 109

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 77

Data Lake Data Analytics Analytics Data Processing 77

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

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 79

Snapshot Metadata Measurement Data Warehouse 79

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 64

Data Architecture Metadata Data Lake Snapshot 64

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 78

Data Warehouse Visualization Snapshot Data-driven 78

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 76

Metrics Data Warehouse Dashboards Snapshot 76

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 69

Data Lake Measurement Visualization Data Architecture 69

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 75

Management Metadata Testing Internet of Things 75

AWS Big Data

JULY 19, 2023

We show how to perform extract, transform, and load (ELT), an integration process focused on getting the raw data from a data lake into a staging layer to perform the modeling. Stage the source data Before we can create and load the dimensions table, we need source data. There are seven different dimension types.

Modeling 66

Modeling Sales Data Warehouse Snapshot 66

AWS Big Data

AUGUST 16, 2023

Iceberg manages large collections of files as tables, and it supports modern analytical data lake operations such as record-level insert, update, delete, and time travel queries. Iceberg also helps guarantee data correctness under concurrent write scenarios. We use a sample JSON file as input to Amazon DynamoDB.

Data Lake 68

Data Lake Metadata Testing Snapshot 68

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 79

Data Lake Dashboards Metrics Metadata 79

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 346

IT Testing Experimentation Software 346

AWS Big Data

MARCH 20, 2023

Top line revenue refers to the total value of sales of an organization’s services or products. The data from the S3 data lake is used for batch processing and analytics through Amazon EMR and Amazon Redshift. Kinesis Data Analytics for Apache Flink provides the underlying infrastructure for your Apache Flink applications.

Analytics 52

Analytics Slice and Dice Data Processing Data Lake 52

AWS Big Data

MAY 19, 2023

Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS , data warehouses ( Amazon Redshift ), search ( Amazon OpenSearch Service ), NoSQL ( Amazon DynamoDB ), machine learning ( Amazon SageMaker ), and more.

Machine Learning 79

Machine Learning Metrics Management Big Data 79

Cloudera

MARCH 23, 2022

Apache Iceberg snapshot and time-travel features can help analysts and auditors to easily look back in time and analyze the data with the simplicity of SQL. . Financial regulation. Many financial and highly regulated industries want a way to look back and even restore tables to specific moments in time. Reproducibility for ML Ops.

Metadata 88

Metadata Data Architecture Machine Learning Cost-Benefit 88

AWS Big Data

NOVEMBER 29, 2023

We have seen a strong customer demand to expand its scope to cloud-based data lakes because data lakes are increasingly the enterprise solution for large-scale data initiatives due to their power and capabilities. Let’s say that this company is located in Europe and the data product must comply with the GDPR.

Data Lake 99

Data Lake Management Metrics Data Warehouse 99

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 76

Data Warehouse Data Lake OLAP Cost-Benefit 76

AWS Big Data

JANUARY 27, 2023

For example, the Flink FileSystem connector has FileSystemTableFactory to read/write data in Hadoop Distributed File System (HDFS) or Amazon Simple Storage Service (Amazon S3), the Flink HBase connector has HBase2DynamicTableFactory to read/write data in HBase, and the Flink Kafka connector has KafkaDynamicTableFactory to read/write data in Kafka.

Data Lake 60

Data Lake Metadata Business Analysis Data-driven 60

AWS Big Data

JULY 28, 2023

Amazon Redshift now makes it easier for you to run queries in AWS data lakes by automatically mounting the AWS Glue Data Catalog. You no longer have to create an external schema in Amazon Redshift to use the data lake tables cataloged in the Data Catalog.

Data Lake 64

Data Lake Data Governance Data Warehouse Modeling 64