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 117

Snapshot Data Lake Metadata Recreation/Entertainment 117

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 108

Optimization Snapshot Metadata Metrics 108

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.

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Data Lake Analytics Snapshot Data Quality 88

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.

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Data Lake Data Processing Metadata Snapshot 97

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 110

Data Lake Sales Data Warehouse Snapshot 110

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 84

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 107

Snapshot Data Lake Metadata Optimization 107

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 117

Optimization Snapshot Data Lake Metadata 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.

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Data Lake Snapshot Metadata Optimization 100

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 77

Analytics Metadata Snapshot Data-driven 77

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.

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Snapshot Data Lake Testing Strategy 88

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 90

Snapshot Metadata Cost-Benefit Data Warehouse 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 67

Data Architecture Metadata Data Lake Snapshot 67

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

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 79

Data Lake Metadata Snapshot Management 79

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 103

Metadata Data Lake Management Internet of Things 103

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 81

Data Warehouse Snapshot Metadata Cost-Benefit 81

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.

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Snapshot Data Warehouse Data Processing Strategy 75

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

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 86

Data Warehouse Management Snapshot Data Lake 86

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 90

Enterprise Data Warehouse Snapshot Cost-Benefit 90

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 .

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Metadata Data Warehouse Snapshot Machine Learning 79

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 84

Snapshot Metadata Measurement Data Warehouse 84

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 97

Data Lake Unstructured Data Management Snapshot 97

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 91

Metadata Data Architecture Machine Learning Cost-Benefit 91

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 117

Analytics IoT Data-driven Snapshot 117

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 84

Data Lake Dashboards Metrics Metadata 84

AWS Big Data

JUNE 12, 2023

After the processed data is stored in Amazon S3, we create an AWS Glue crawler to create a Data Catalog table that acts as a metadata layer for the data. The table can be queried using Amazon Athena , a serverless, interactive query service that enables running SQL-like queries on data stored in Amazon S3.

Management 80

Management Metadata Testing Internet of Things 80

AWS Big Data

AUGUST 16, 2023

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. Iceberg also helps guarantee data correctness under concurrent write scenarios. We fetch the metadata of the users_xxxxxx table from Athena.

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Data Lake Metadata Testing Snapshot 72

AWS Big Data

JANUARY 27, 2023

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. 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. Refer to Catalogs for more information.

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Data Lake Metadata Business Analysis Data-driven 62

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