AWS Big Data

APRIL 3, 2024

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

Data Lake 106

Data Lake Snapshot Metadata Data Architecture 106

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. and Delta Lake 2.3.0. Apache Iceberg 1.2.0,

Data Lake 125

Data Lake Metadata Statistics Optimization 125

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 122

Data Lake Data Warehouse Visualization Snapshot 122

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 116

Data Lake Sales Data Warehouse Snapshot 116

AWS Big Data

DECEMBER 4, 2024

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

Data Integration 98

Data Integration Data Lake Statistics Data-driven 98

AWS Big Data

JULY 29, 2024

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.

Metadata 119

Metadata Snapshot Data Lake Metrics 119

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. The examples are run on a Jupyter Notebook environment attached to the EMR cluster.

Data Lake 92

Data Lake Snapshot Metadata Optimization 92

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. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

Data Lake 94

Data Lake Analytics Snapshot Data Quality 94

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 104

Data Lake Data Processing Metadata Snapshot 104

AWS Big Data

DECEMBER 12, 2024

In the context of comprehensive data governance, Amazon DataZone offers organization-wide data lineage visualization using Amazon Web Services (AWS) services, while dbt provides project-level lineage through model analysis and supports cross-project integration between data lakes and warehouses.

Snapshot 79

Snapshot Recreation/Entertainment Experimentation Data Lake 79

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 81

Data Lake Snapshot Optimization Data Transformation 81

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 124

Snapshot Data Lake Metadata Recreation/Entertainment 124

AWS Big Data

SEPTEMBER 12, 2024

The AWS Glue Data Catalog now enhances managed table optimization of Apache Iceberg tables by automatically removing data files that are no longer needed. Iceberg creates a new version called a snapshot for every change to the data in the table. As more table changes are made, more data files are created.

Optimization 115

Optimization Snapshot Metadata Metrics 115

O'Reilly on Data

OCTOBER 19, 2021

Since software engineers manage to build ordinary software without experiencing as much pain as their counterparts in the ML department, it begs the question: should we just start treating ML projects as software engineering projects as usual, maybe educating ML practitioners about the existing best practices? Orchestration. Versioning.

IT 363

IT Testing Experimentation Software 363

AWS Big Data

DECEMBER 9, 2024

In todays data-driven world, tracking and analyzing changes over time has become essential. As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis.

Snapshot 64

Snapshot Data Warehouse Data Lake Data Quality 64

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 122

Optimization Snapshot Data Lake Metadata 122

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. For more information, refer to Amazon S3: Allows read and write access to objects in an S3 Bucket.

Snapshot 115

Snapshot Data Lake Metadata Optimization 115

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. You can define your own key and value for your resource tag, so that you can easily manage and filter your resources. Tags allows you to assign metadata to your AWS resources. Create cost reports.

Data Warehouse 92

Data Warehouse Management Snapshot Data Lake 92

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 Big Data 81

AWS Big Data

JULY 3, 2023

Index rebalancing arbitrage takes advantage of short-term price discrepancies resulting from ETF managers’ efforts to minimize index tracking error. Amazon Simple Storage Service (Amazon S3) is a popular cloud-based object storage service that can be used as the foundation for building a data lake.

Snapshot 95

Snapshot Data Lake Testing Strategy 95

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 117

Data Lake Management Metrics Data Warehouse 117

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.

Data Lake 107

Data Lake Snapshot Metadata Optimization 107

AWS Big Data

JULY 8, 2024

Amazon Managed Service for Apache Flink offers a fully managed, serverless experience in running Apache Flink applications and now supports Apache Flink 1.19.1 , the latest stable version of Apache Flink at the time of writing. Managed Service for Apache Flink currently uses the Python 3.11 support Python 3.11 Python 3.11

Management 105

Management Dashboards Consulting Snapshot 105

AWS Big Data

MARCH 20, 2023

In the first post of this series , we described how AWS Glue for Apache Spark works with Apache Hudi, Linux Foundation Delta Lake, and Apache Iceberg datasets tables using the native support of those data lake formats. Even without prior experience using Hudi, Delta Lake or Iceberg, you can easily achieve typical use cases.

Visualization 88

Visualization Data Lake Snapshot Big Data 88

AWS Big Data

JANUARY 27, 2023

The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Apache Flink is a widely used data processing engine for scalable streaming ETL, analytics, and event-driven applications. Apache Hudi also has its own catalog management.

Data Lake 67

Data Lake Metadata Business Analysis Data-driven 67

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.

Data Lake 107

Data Lake Snapshot Big Data Data-driven 107

AWS Big Data

FEBRUARY 12, 2024

About Redshift and some relevant features for the use case Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

Analytics 114

Analytics Data Warehouse Snapshot Cost-Benefit 114

AWS Big Data

MAY 15, 2024

It is a serverless service, eliminating the need for infrastructure management and costing you only for the queries you run. By extracting detailed information from CloudTrail and querying it using Athena, this solution streamlines the process of data collection, analysis, and reporting of EIP usage within an AWS account.

Snapshot 104

Snapshot Data Lake Optimization Reporting 104

AWS Big Data

AUGUST 9, 2024

This trend is no exception for Dafiti , an ecommerce company that recognizes the importance of using data to drive strategic decision-making processes. Amazon Redshift is widely used for Dafiti’s data analytics, supporting approximately 100,000 daily queries from over 400 users across three countries. TB of data.

Data Lake 107

Data Lake Analytics Data Warehouse Data-driven 107

AWS Big Data

JUNE 27, 2024

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers. For additional details, refer to Automated snapshots.

Snapshot 81

Snapshot Data Warehouse Data Processing Strategy 81

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 97

Enterprise Data Warehouse Snapshot Cost-Benefit 97

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 105

Data Processing Data Lake Data Warehouse Optimization 105

AWS Big Data

MARCH 25, 2024

In this post, we discuss why data streaming is a crucial component of generative AI applications due to its real-time nature. In-context learning LLMs are trained with point-in-time data and have no inherent ability to access fresh data at inference time. For more information, refer to Dynamic Tables.

Data Lake 105

Data Lake Unstructured Data Management Snapshot 105

Cloudera

APRIL 3, 2023

Every table change creates an Iceberg snapshot, this helps to resolve concurrency issues and allows readers to scan a stable table state every time. During queries the query engines scan both the data files and delete files belonging to the same snapshot and merge them together (i.e. ID, TBL_ICEBERG_PART_2.NAME,

Data Warehouse 82

Data Warehouse Snapshot Metadata Cost-Benefit 82

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. AWS Glue crawlers will extract schema information and update the location of Iceberg metadata and schema updates in the Data Catalog. Choose Next.

Data Lake 86

Data Lake Metadata Snapshot Management 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

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. Automation enabled Uber to grow to their current state with more than 256 petabytes of data, 3,000 nodes and 12 clusters.

OLAP 86

OLAP Data Lake Data-driven Online Analytical Processing 86