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. availability. Note the configuration parameters s3.write.tags.write-tag-name write.tags.write-tag-name and s3.delete.tags.delete-tag-name

Data Lake 98

Data Lake Snapshot Metadata Optimization 98

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 98

Data Lake Snapshot Optimization Data Transformation 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

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 103

Data Lake Testing Snapshot Big Data 103

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

JULY 3, 2023

Terminology Let’s first discuss some of the terminology used in this post: Research data lake on Amazon S3 – A data lake is a large, centralized repository that allows you to manage all your structured and unstructured data at any scale.

Snapshot 105

Snapshot Data Lake Testing Strategy 105

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

JUNE 27, 2024

With built-in features such as automated snapshots and cross-Region replication, you can enhance your disaster resilience with Amazon Redshift. Test out the disaster recovery plan by simulating a failover event in a non-production environment. Snapshots are point-in-time backups of the Redshift data warehouse.

Snapshot 93

Snapshot Data Warehouse Data Processing Strategy 93

AWS Big Data

AUGUST 9, 2024

These processes retrieve data from around 90 different data sources, resulting in updating roughly 2,000 tables in the data warehouse and 3,000 external tables in Parquet format, accessed through Amazon Redshift Spectrum and a data lake on Amazon Simple Storage Service (Amazon S3). TB of data.

Data Lake 111

Data Lake Analytics Data Warehouse Data-driven 111

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 122

Data Lake Management Metrics Data Warehouse 122

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 111

Enterprise Data Warehouse Snapshot Cost-Benefit 111

AWS Big Data

NOVEMBER 10, 2023

These tables are then joined with tables from the Enterprise Data Lake (EDL) at runtime. During feature development, data engineers require a seamless interface to the EDW. Previous solution process In the previous solution, product team data engineers spent 30 minutes per run to manually expose Redshift data to Spark.

Data Processing 111

Data Processing Data Lake Data Warehouse Optimization 111

Cloudera

APRIL 3, 2023

Cloudera Contributors: Ayush Saxena, Tamas Mate, Simhadri Govindappa Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

Data Warehouse 82

Data Warehouse Snapshot Metadata Cost-Benefit 82

IBM Big Data Hub

SEPTEMBER 25, 2023

Uber understood that digital superiority required the capture of all their transactional data, not just a sampling. They stood up a file-based data lake alongside their analytical database. Because much of the work done on their data lake is exploratory in nature, many users want to execute untested queries on petabytes of data.

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86

Cloudera

FEBRUARY 8, 2024

Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows. Incremental and full rebuild of materialized view We will insert rows into the base table and examine how the materialized view can be updated to reflect the new data.

Snapshot 91

Snapshot Metadata Cost-Benefit Data Warehouse 91

AWS Big Data

AUGUST 1, 2024

This will enable right-sizing the Redshift data warehouse to meet workload demands cost-effectively. Thorough testing and performance optimization will facilitate a smooth transition with minimal disruption to end-users, fostering exceptional user experiences and satisfaction.

Data Warehouse 115

Data Warehouse KPI Optimization Cost-Benefit 115

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

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 25, 2023

If any dashboard query takes more than a minute, it could indicate a poorly written query or a query that hasn’t been tested well, and has incorrectly been released to production. The following screenshot shows the metrics available at the snapshot storage level. You know that dashboard queries typically complete in under a minute.

Metrics 98

Metrics Data Warehouse Dashboards Snapshot 98

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. We deploy Debezium MySQL source Kafka connector on Amazon MSK Connect.

Data Warehouse 116

Data Warehouse Snapshot Data Processing Internet of Things 116

Cloudera

DECEMBER 15, 2022

Improve performance and overall manageability of Iceberg tables using the new table maintenance capabilities such as expiring old snapshots and removing their metadata, and compaction to combine small files for more efficient data processing. Maintaining performance and manageability with improved table maintenance .

Metadata 80

Metadata Data Warehouse Snapshot Machine Learning 80

AWS Big Data

MARCH 3, 2023

Tricentis is the global leader in continuous testing for DevOps, cloud, and enterprise applications. Speed changes everything, and continuous testing across the entire CI/CD lifecycle is the key. Tricentis instills that confidence by providing software tools that enable Agile Continuous Testing (ACT) at scale.

Software 52

Software Data Lake Testing Cost-Benefit 52

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. Test your new function with the following code, which returns the coordinates of the White House in Washington, DC: select public.f_geocode_address('1600 Pennsylvania Ave.','Washington','DC','20500','USA');

Data Warehouse 114

Data Warehouse Visualization Snapshot Data-driven 114

AWS Big Data

JUNE 12, 2023

test-schema-registry MSKSchemaName Name of the schema. test_payload_schema GlueDataBaseName Name of the AWS Glue Data Catalog database. test_glue_database GlueTableName Name of the AWS Glue Data Catalog table. 5 S3BucketForOutput Bucket name for writing data from the AWS Glue job. Refer to the first stack’s output.

Management 98

Management Metadata Internet of Things Testing 98

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. On the Code tab, choose Test , then Configure test event.

Data Lake 97

Data Lake Metadata Testing Snapshot 97

AWS Big Data

MAY 9, 2025

As data use cases become more complex, data engineering teams require sophisticated tooling to handle versioning, increasing data volumes, and schema changes across multiple data sources and applications. For example, they might need to compare historical snapshots with current data to analyze trends over time.

Snapshot 91

Snapshot Analytics Data-driven Data Processing 91

AWS Big Data

DECEMBER 19, 2024

Data lakes were originally designed to store large volumes of raw, unstructured, or semi-structured data at a low cost, primarily serving big data and analytics use cases. Enabling automatic compaction on Iceberg tables reduces metadata overhead on your Iceberg tables and improves query performance.

Data Lake 98

Data Lake IoT Metadata Testing 98

AWS Big Data

NOVEMBER 6, 2024

Second, because traditional data warehousing approaches are unable to keep up with the volume, velocity, and variety of data, engineering teams are building data lakes and adopting open data formats such as Parquet and Apache Iceberg to store their data. Choose Send data.

Metadata 116

Metadata Data Lake Management Internet of Things 116

Cloudera

DECEMBER 3, 2024

Cloudera’s open data lakehouse, powered by Apache Iceberg, solves the real-world big data challenges mentioned above by providing a unified, curated, shareable, and interoperable data lake that is accessible by a wide array of Iceberg-compatible compute engines and tools. spark.sql(SELECT * FROM airlines_data.carriers).show()

Metadata 84

Metadata Data Warehouse ROI Snapshot 84