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. parquet") df.sortWithinPartitions("review_date").writeTo("dev.db.amazon_reviews_iceberg").append()

Data Lake 98

Data Lake Snapshot Metadata Optimization 98

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 122

Data Lake Sales Data Warehouse Snapshot 122

AWS Big Data

SEPTEMBER 12, 2024

Along with the Glue Data Catalog’s automated compaction feature, these storage optimizations can help you reduce metadata overhead, control storage costs, and improve query performance. Iceberg creates a new version called a snapshot for every change to the data in the table.

Optimization 128

Optimization Snapshot Metadata Metrics 128

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

NOVEMBER 26, 2024

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. Eventually, transactional data lakes emerged to add transactional consistency and performance of a data warehouse to the data lake.

Metadata 105

Metadata Data Lake Snapshot Data Warehouse 105

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.

Snapshot 126

Snapshot Data Lake Metadata Optimization 126

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 98

Data Lake Analytics Snapshot Data Quality 98

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

DECEMBER 4, 2024

Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. By providing a standardized framework for data representation, open table formats break down data silos, enhance data quality, and accelerate analytics at scale.

Snapshot 84

Snapshot Metadata Data Lake Optimization 84

AWS Big Data

DECEMBER 9, 2024

Today, many customers build data quality validation pipelines using its Data Quality Definition Language (DQDL) because with static rules, dynamic rules , and anomaly detection capability , its fairly straightforward. One of its key features is the ability to manage data using branches.

Data Quality 124

Data Quality Publishing Snapshot Data Lake 124

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 115

Data Lake Snapshot Metadata Optimization 115

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. This is where the tagging feature in Apache Iceberg comes in handy.

Snapshot 105

Snapshot Data Lake Testing Strategy 105

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 90

Optimization Snapshot Data Lake Cost-Benefit 90

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 90

Data Architecture Metadata Data Lake Snapshot 90

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 52

Metadata Data-driven Snapshot Data Lake 52

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. Iceberg captures metadata information on the state of datasets as they evolve and change over time. Choose Create.

Data Lake 97

Data Lake Metadata Snapshot Management 97

Cloudera

FEBRUARY 8, 2024

The snapshotId of the source tables involved in the materialized view are also maintained in the metadata. Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows. Furthermore, it is partitioned on the d_year column.

Snapshot 96

Snapshot Metadata Cost-Benefit Data Warehouse 96

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 115

Data Lake Snapshot Big Data Data-driven 115

AWS Big Data

NOVEMBER 21, 2024

The AWS Glue Data Catalog supports automatic table optimization of Apache Iceberg tables, including compaction , snapshots, and orphan data management. The data compaction optimizer constantly monitors table partitions and kicks off the compaction process when the threshold is exceeded for the number of files and file sizes.

Optimization 111

Optimization Snapshot Metadata Software 111

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 113

Data Integration Data Lake Statistics Data-driven 113

AWS Big Data

MARCH 27, 2023

Developers, data scientists, and analysts can work across databases, data warehouses, and data lakes to build reporting and dashboarding applications, perform real-time analytics, share and collaborate on data, and even build and train machine learning (ML) models with Redshift Serverless.

Data Warehouse 98

Data Warehouse Management Snapshot Data Lake 98

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 110

Enterprise Data Warehouse Snapshot Cost-Benefit 110

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. b64decode(record['data']).decode('utf-8')

Metadata 115

Metadata Data Lake Management Internet of Things 115

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. To develop your disaster recovery plan, you should complete the following tasks: Define your recovery objectives for downtime and data loss (RTO and RPO) for data and metadata.

Snapshot 92

Snapshot Data Warehouse Data Processing Strategy 92

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. In a bit more detail, Iceberg V1 added support for creating, updating, deleting and inserting data into tables. These files store the file paths and positions of the deleted records.

Data Warehouse 87

Data Warehouse Snapshot Metadata Cost-Benefit 87

Cloudera

DECEMBER 15, 2022

With in-place table migration, you can rapidly convert to Iceberg tables since there is no need to regenerate data files. Only metadata will be regenerated. Newly generated metadata will then point to source data files as illustrated in the diagram below. . Data quality using table rollback. Metadata management .

Metadata 85

Metadata Data Warehouse Snapshot Machine Learning 85

AWS Big Data

JULY 28, 2023

Tagging Consider tagging your Amazon Redshift resources to quickly identify which clusters and snapshots contain the PII data, the owners, the data retention policy, and so on. Tags provide metadata about resources at a glance. Redshift resources, such as namespaces, workgroups, snapshots, and clusters can be tagged.

Snapshot 98

Snapshot Metadata Measurement Data Warehouse 98

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 119

Data Lake Unstructured Data Management Snapshot 119

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.

Optimization 59

Optimization Strategy Snapshot Metadata 59

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

Cloudera

MARCH 23, 2022

The table information (such as schema, partition) is stored as part of the metadata (manifest) file separately, making it easier for applications to quickly integrate with the tables and the storage formats of their choice. Iceberg, on the other hand, is an open table format that works with open file formats to avoid this coupling.

Metadata 97

Metadata Data Architecture Machine Learning Cost-Benefit 97

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 89

Metadata Data Warehouse ROI Snapshot 89

AWS Big Data

JANUARY 8, 2024

Stream Processing – An application created with Amazon Managed Service for Apache Flink can read the records from the data stream to detect and clean any errors in the time series data and enrich the data with specific metadata to optimize operational analytics.

Analytics 134

Analytics IoT Data-driven Snapshot 134

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 90

Analytics Metadata Snapshot Data-driven 90

AWS Big Data

DECEMBER 9, 2024

With change log view, we can easily track insertions, updates, and deletions, giving us a complete picture of how our data has evolved. For our heater example, Icebergs change log view would allow us to effortlessly retrieve a timeline of all price changes, complete with timestamps and other relevant metadata, as shown in the following table.

Snapshot 64

Snapshot Data Warehouse Data Lake Data Quality 64

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. By using features like Icebergs compaction, OTFs streamline maintenance, making it straightforward to manage object and metadata versioning at scale.

Data Lake 52

Data Lake IoT Metadata Testing 52

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 98

Data Lake Dashboards Metrics Metadata 98