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 74

Data Lake Testing Snapshot Sales 74

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.

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Optimization Snapshot Data Lake Cost-Benefit 83

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.

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AWS Big Data

NOVEMBER 22, 2023

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. Create your S3 bucket if you do not have it.

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Data Lake Snapshot Metadata Optimization 100

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.

Strategy 59

Strategy Optimization Snapshot Metadata 59

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.

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Data Lake Snapshot Big Data Data-driven 100

Cloudera

FEBRUARY 8, 2024

Overview This blog post describes support for materialized views for the Iceberg table format. It brings the reliability and simplicity of SQL tables to big data while enabling engines like Hive, Impala, Spark, Trino, Flink, and Presto to work with the same tables at the same time. These tables are created as Iceberg tables.

Snapshot 89

Snapshot Metadata Cost-Benefit Data Warehouse 89

Cloudera

APRIL 3, 2023

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

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Data Warehouse Snapshot Metadata Cost-Benefit 80

Cloudera

NOVEMBER 15, 2024

A Better Way Forward: Cloudera’s Open Data Lakehouse Cloudera offers a solution to these challenges with its open data lakehouse, which combines the flexibility and scalability of data lake storage with data warehouse functionality to unify and simplify the management of cyber log data.

Analytics 83

Analytics Metadata Snapshot Data-driven 83

AWS Big Data

MARCH 23, 2023

For this blog our “primary” workgroup is using Athena engine version 3. Data producer setup In this section, we present the steps to set up the data producer. Register the S3 path storing the table using Lake Formation We register the S3 full path in Lake Formation: Navigate to the Lake Formation console.

Interactive 69

Interactive Snapshot Data Lake Software 69

Cloudera

NOVEMBER 15, 2024

A Better Way Forward: Cloudera’s Open Data Lakehouse Cloudera offers a solution to these challenges with its open data lakehouse, which combines the flexibility and scalability of data lake storage with data warehouse functionality to unify and simplify the management of cyber log data.

Analytics 59

Analytics Metadata Snapshot Data-driven 59

Cloudera

MARCH 5, 2024

With built-in features like time travel, schema evolution, and streamlined data discovery, Iceberg empowers data teams to enhance data lake management while upholding data integrity. Learn more about the next generation of Cloudera Data Platform for Private Cloud.

Snapshot 85

Snapshot Data Lake Enterprise Data Governance 85

AWS Big Data

AUGUST 2, 2023

In this blog, we will walk through how we can apply existing enterprise data to better understand and estimate Scope 1 carbon footprint using Amazon Simple Storage Service (S3) and Amazon Athena , a serverless interactive analytics service that makes it easy to analyze data using standard SQL.

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Data Lake Measurement Visualization Data Architecture 72

AWS Big Data

JULY 8, 2024

Extending checkpoint intervals allows Apache Flink to prioritize processing throughput over frequent state snapshots, thereby improving efficiency and performance. You can find more details about recent releases from the Apache Flink blog and release notes: Amazon Managed Service for Apache Flink 1.19 release notes Apache Flink 1.19.0

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Management Consulting Snapshot Dashboards 98

Cloudera

SEPTEMBER 12, 2022

We have delivered the performance and reliability of the data warehouse with the flexibility and scale of a data lake with our data service engines and the Hive metastore. Applying the Iceberg table format to all the organization’s data in the data lake makes it more performant and usable at scale.

Unstructured Data 74

Unstructured Data Data Architecture Data Lake Snapshot 74

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 .

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Metadata Data Warehouse Snapshot Machine Learning 78

AWS Big Data

JANUARY 8, 2024

Therefore, it is critical for organizations to embrace a low-latency, scalable, and reliable data streaming infrastructure to deliver real-time business applications and better customer experiences. It can receive the events from an input Kinesis data stream and route the resulting stream to an output data stream.

Analytics 117

Analytics IoT Data-driven Snapshot 117

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. We discuss implementing dimensions and facts within Amazon Redshift. Solution overview The following diagram illustrates the solution architecture.

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Modeling Sales Data Warehouse Snapshot 69

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. For Type , choose Spark.

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Data Lake Dashboards Metrics Metadata 84

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. . The post 5 Reasons to Use Apache Iceberg on Cloudera Data Platform (CDP) appeared first on Cloudera Blog. Financial regulation. Reproducibility for ML Ops.

Metadata 90

Metadata Data Architecture Machine Learning Cost-Benefit 90

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 90

Machine Learning Metrics Big Data Management 90

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.

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Data Lake Management Metrics Data Warehouse 110

IBM Big Data Hub

SEPTEMBER 25, 2023

But what most people don’t realize is that behind the scenes, Uber is not just a transportation service; it’s a data and analytics powerhouse. Every day, millions of riders use the Uber app, unwittingly contributing to a complex web of data-driven decisions. They ingest data in snapshots from operational systems.

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