AWS Big Data

DECEMBER 4, 2024

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.

Data Integration 98

Data Integration Data Lake Statistics Data-driven 98

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . 3: Open Performance.

Metadata 92

Metadata Data Architecture Machine Learning Cost-Benefit 92

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. Non-blocking automatic table services (for example, compaction) that don’t impact writers or readers.

Data Lake 125

Data Lake Metadata Statistics Optimization 125

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. Expiring old snapshots – This operation provides a way to remove outdated snapshots and their associated data files, enabling Orca to maintain low storage costs.

Data Lake 94

Data Lake Analytics Snapshot Data Quality 94

AWS Big Data

MARCH 10, 2023

Apache Iceberg overview Iceberg is an open-source table format that brings the power of SQL tables to big data files. It enables ACID transactions on tables, allowing for concurrent data ingestion, updates, and queries, all while using familiar SQL. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 116

Data Lake Sales Data Warehouse Snapshot 116

Cloudera

OCTOBER 11, 2022

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. First you need the data analytics, data management, and data science tools.

Data Science 94

Data Science Snapshot Data Warehouse Metadata 94

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker.

Data Lake 104

Data Lake Data Processing Metadata Snapshot 104

AWS Big Data

JANUARY 8, 2024

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. State snapshot in Amazon S3 – You can store the state snapshot in Amazon S3 for tracking.

Analytics 124

Analytics IoT Data-driven Snapshot 124

AWS Big Data

APRIL 3, 2024

If the asset has AWS Glue Data Quality enabled, you can now quickly visualize the data quality score directly in the catalog search pane. By selecting the corresponding asset, you can understand its content through the readme, glossary terms , and technical and business metadata.

Data Quality 104

Data Quality Visualization Metadata Metrics 104

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. Data streaming enables you to ingest data from a variety of databases across various systems.

Data Lake 105

Data Lake Unstructured Data Management Snapshot 105

Alation

JULY 28, 2022

What Are the Biggest Drivers of Cloud Data Warehousing? It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. There are tools to replicate and snapshot data, plus tools to scale and improve performance.”

Data Warehouse 69

Data Warehouse Cost-Benefit Data-driven Data Governance 69

AWS Big Data

MARCH 3, 2023

Athena supports reading native Delta tables and therefore we can read the data successfully even though the Data Catalog shows only a single array column. If you need the individual column-level metadata to be available in the Data Catalog, run an AWS Glue crawler periodically to keep the AWS Glue metadata updated.

Data Lake 91

Data Lake Dashboards Metrics Metadata 91

AWS Big Data

DECEMBER 20, 2024

Solution overview The basic concept of the modernization project is to create metadata-driven frameworks, which are reusable, scalable, and able to respond to the different phases of the modernization process. These phases are: data orchestration, data migration, data ingestion, data processing, and data maintenance.

Metadata 112

Metadata Data-driven Snapshot Data Lake 112

Cloudera

DECEMBER 3, 2024

REST Catalog Value Proposition It provides open, metastore-agnostic APIs for Iceberg metadata operations, dramatically simplifying the Iceberg client and metastore/engine integration. It provides real time metadata access by directly integrating with the Iceberg-compatible metastore. spark.sql(SELECT * FROM airlines_data.carriers).show()

Metadata 84

Metadata Data Warehouse ROI Snapshot 84

DataKitchen

AUGUST 8, 2023

Data Observability leverages five critical technologies to create a data awareness AI engine: data profiling, active metadata analysis, machine learning, data monitoring, and data lineage. Like an apartment blueprint, Data lineage provides a written document that is only marginally useful during a crisis.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

DataKitchen

JANUARY 23, 2023

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

Testing 130

Testing Data Governance Data Quality Data-driven 130