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 81

Data Lake Testing Snapshot Sales 81

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. It will never remove files that are still required by a non-expired snapshot.

Snapshot 115

Snapshot Data Lake Metadata Optimization 115

AWS Big Data

MAY 30, 2023

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Traditional batch ingestion and processing pipelines that involve operations such as data cleaning and joining with reference data are straightforward to create and cost-efficient to maintain. options(**additional_options).mode("append").save(s3_output_folder)

Data Lake 88

Data Lake Data Analytics Analytics Data Processing 88

AWS Big Data

AUGUST 9, 2024

With the ever-increasing volume of data available, Dafiti faces the challenge of effectively managing and extracting valuable insights from this vast pool of information to gain a competitive edge and make data-driven decisions that align with company business objectives. TB of data. We started with 115 dc2.large

Data Lake 107

Data Lake Analytics Data Warehouse Data-driven 107

IBM Big Data Hub

SEPTEMBER 25, 2023

Presto was able to achieve this level of scalability by completely separating analytical compute from data storage. Presto is an open source distributed SQL query engine for data analytics and the data lakehouse, designed for running interactive analytic queries against datasets of all sizes, from gigabytes to petabytes.

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86

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 73

Data Architecture Metadata Data Lake Snapshot 73

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 details, refer to Create a low-latency source-to-data lake pipeline using Amazon MSK Connect, Apache Flink, and Apache Hudi.

Data Lake 105

Data Lake Unstructured Data Management Snapshot 105

AWS Big Data

JANUARY 8, 2024

This is the first post to a blog series that offers common architectural patterns in building real-time data streaming infrastructures using Kinesis Data Streams for a wide range of use cases. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 124

Analytics IoT Data-driven Snapshot 124

AWS Big Data

AUGUST 2, 2023

Using existing analytics tools such as Amazon Athena and Amazon QuickSight an organization can gain insight into its estimated carbon footprint. 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.

Data Lake 79

Data Lake Measurement Visualization Data Architecture 79

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. He works with AWS customers to design and build real time data processing systems. Vishal Khatri is a Sr.

Data Warehouse 111

Data Warehouse Snapshot Data Processing Internet of Things 111

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.

Data Lake 91

Data Lake Dashboards Metrics Metadata 91

Cloudera

NOVEMBER 15, 2024

However, there is a fundamental challenge standing in the way of being successful: data. By breaking down data silos and integrating log data from multiple sources, Cloudera empowers defenders with the real-time analytics to respond to threats swiftly.

Analytics 59

Analytics Metadata Snapshot Data-driven 59

AWS Big Data

MARCH 20, 2023

In this post, we share how Poshmark improved CX and accelerated revenue growth by using a real-time analytics solution. High-level challenge: The need for real-time analytics Previous efforts at Poshmark for improving CX through analytics were based on batch processing of analytics data and using it on a daily basis to improve CX.

Analytics 52

Analytics Slice and Dice Data Processing Data Lake 52

AWS Big Data

MARCH 3, 2023

From detailed design to a beta release, Tricentis had customers expecting to consume data from a data lake specific to only their data, and all of the data that had been generated for over a decade. Data export As stated earlier, some customers want to get an export of their test data and create their data lake.

Software 52

Software Data Lake Testing Cost-Benefit 52

AWS Big Data

AUGUST 1, 2024

We can determine the following are needed: An open data format ingestion architecture processing the source dataset and refining the data in the S3 data lake. This requires a dedicated team of 3–7 members building a serverless data lake for all data sources. Vijay Bagur is a Sr.

Data Warehouse 100

Data Warehouse KPI Optimization Cost-Benefit 100

AWS Big Data

JUNE 12, 2023

Organizations across the world are increasingly relying on streaming data, and there is a growing need for real-time data analytics, considering the growing velocity and volume of data being collected. Step 6} $ REGISTRY_NAME={VAL_OF_GlueSchemaRegistryName - Ref. Step 6} $ SCHEMA_NAME={VAL_OF_SchemaName– Ref.

Management 87

Management Metadata Internet of Things Testing 87

AWS Big Data

AUGUST 16, 2023

Apache Iceberg is an open table format for very large analytic datasets. 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. We use a sample JSON file as input to Amazon DynamoDB.

Data Lake 79

Data Lake Metadata Testing Snapshot 79

Corinium

APRIL 25, 2019

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. In data-driven organizations, data is flowing.

Insurance 150

Insurance Risk IoT Data-driven 150

Cloudera

OCTOBER 10, 2024

It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse. Table Cleanup: As tables grow, they often accumulate unused data files, manifest files, and snapshots that aren’t needed anymore.

Optimization 84

Optimization Snapshot Data Lake Cost-Benefit 84

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 92

Data Lake IoT Metadata Testing 92

AWS Big Data

FEBRUARY 18, 2025

To optimize their security operations, organizations are adopting modern approaches that combine real-time monitoring with scalable data analytics. They are using data lake architectures and Apache Iceberg to efficiently process large volumes of security data while minimizing operational overhead.

Snapshot 76

Snapshot Optimization Data Lake Metadata 76

Cloudera

NOVEMBER 15, 2024

However, there is a fundamental challenge standing in the way of being successful: data. By breaking down data silos and integrating log data from multiple sources, Cloudera empowers defenders with the real-time analytics to respond to threats swiftly.

Analytics 84

Analytics Metadata Snapshot Data-driven 84

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.

Metadata 109

Metadata Data Lake Management Internet of Things 109

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.

Metadata 84

Metadata Data Warehouse ROI Snapshot 84