AWS Big Data

APRIL 3, 2024

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

Data Lake 105

Data Lake Snapshot Metadata Data Architecture 105

AWS Big Data

JANUARY 24, 2024

These formats enable ACID (atomicity, consistency, isolation, durability) transactions, upserts, and deletes, and advanced features such as time travel and snapshots that were previously only available in data warehouses. It will never remove files that are still required by a non-expired snapshot.

Snapshot 114

Snapshot Data Lake Metadata Optimization 114

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. Document the entire disaster recovery process.

Snapshot 80

Snapshot Data Warehouse Data Processing Strategy 80

erwin

JULY 2, 2020

This matters because, as he said, “By placing the data and the metadata into a model, which is what the tool does, you gain the abilities for linkages between different objects in the model, linkages that you cannot get on paper or with Visio or PowerPoint.” They’re static snapshots of a diagram at some point in time.

Enterprise 122

Enterprise Modeling Metadata Data Governance 122

Cloudera

FEBRUARY 8, 2024

Cloudera Data Warehouse (CDW) running Hive has previously supported creating materialized views against Hive ACID source tables. release and the matching CDW Private Cloud Data Services release, Hive also supports creating, using, and rebuilding materialized views for Iceberg table format.

Snapshot 91

Snapshot Metadata Cost-Benefit Data Warehouse 91

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 96

Enterprise Data Warehouse Snapshot Cost-Benefit 96

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 97

Data Integration Data Lake Statistics Data-driven 97

AWS Big Data

MAY 24, 2023

RIO is really great",date("2023-04-06"),2023)""") You can check the new snapshot is created after this append operation by querying the Iceberg snapshot: spark.sql("""SELECT * FROM dev.db.amazon_reviews_iceberg.snapshots""").show() In that case, we have to query the table with the snapshot-id corresponding to the deleted row.

Data Lake 91

Data Lake Snapshot Metadata Optimization 91

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 106

Data Lake Snapshot Metadata Optimization 106

Sisense

JANUARY 6, 2020

To achieve this, first requires getting the data into a form that delivers insights. Salesforce data is extracted, transformed and loaded into a data warehouse using an ETL tool connected to the data warehouse. Then, use a data model to model the data into a single unified source of truth.

Sales 91

Sales Forecasting Snapshot Management 91

AWS Big Data

MARCH 10, 2023

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 116

Data Lake Sales Data Warehouse Snapshot 116

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 80

Metadata Data Warehouse Snapshot Machine Learning 80

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 72

Data Architecture Metadata Data Lake Snapshot 72

Cloudera

JULY 13, 2023

A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself. The data files and metadata files in Iceberg format are immutable.

Metadata 70

Metadata Snapshot Data Warehouse Statistics 70

AWS Big Data

JULY 28, 2023

Organizations must comply with these requests provided that there are no legitimate grounds for retaining the personal data, such as legal obligations or contractual requirements. Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Tags provide metadata about resources at a glance.

Snapshot 90

Snapshot Metadata Measurement Data Warehouse 90

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. Frequent table maintenance needs to be performed to prevent read performance from degrading over time.

Data Lake 125

Data Lake Metadata Statistics Optimization 125

Cloudera

JULY 14, 2023

While these instructions are carried out for Cloudera Data Platform (CDP), Cloudera Data Engineering, and Cloudera Data Warehouse, one can extrapolate them easily to other services and other use cases as well. Query engines (Impala, Hive, Spark) might mitigate some of these problems by using Iceberg’s metadata files.

Snapshot 70

Snapshot Data Warehouse Metadata Testing 70

Cloudera

OCTOBER 11, 2022

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. But it isn’t just aggregating data for models. Data needs to be prepared and analyzed.

Data Science 94

Data Science Snapshot Data Warehouse Metadata 94

AWS Big Data

FEBRUARY 13, 2023

We live in a data-producing world, and as companies want to become data driven, there is the need to analyze more and more data. These analyses are often done using data warehouses. Status quo before migration Here at OLX Group, Amazon Redshift has been our choice for data warehouse for over 5 years.

Snapshot 73

Snapshot Data Warehouse Analytics Testing 73

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 116

Data Lake Management Metrics Data Warehouse 116

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker.

Data Lake 103

Data Lake Data Processing Metadata Snapshot 103

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

JANUARY 8, 2024

The destination can be an event-driven application for real-time dashboards, automatic decisions based on processed streaming data, real-time altering, and more. Using a data stream in the middle provides the advantage of using the time series data in other processes and solutions at the same time.

Analytics 123

Analytics IoT Data-driven Snapshot 123

AWS Big Data

FEBRUARY 1, 2023

We also used AWS Lambda for data processing. To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. Clients access this data store with an API’s.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

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 Data-driven Snapshot 59

Alation

AUGUST 26, 2021

What Is Data Intelligence? Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” Yet finding data is just the beginning.

Metadata 64

Metadata Data Governance Dashboards Software 64

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. This allows the model to adapt to the latest changes in price and availability. versions).

Data Lake 104

Data Lake Unstructured Data Management Snapshot 104

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. save the built model container, along with metadata like who built or deployed it. Simplified user administration.

Data Science 41

Data Science Snapshot Machine Learning Data Warehouse 41

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 107

Data Lake Snapshot Big Data Data-driven 107

Wayne Yaddow

MARCH 14, 2025

Snapshot testing augments debugging capabilities by recording past table states, facilitating the identification of unforeseen spikes, declines, or abnormalities before their effect on production systems. Workaround: Implement custom metadata tracking scripts or use dbt Clouds freshness monitoring.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

AWS Big Data

MARCH 3, 2023

Athena supports reading native Delta tables and therefore we can read the data successfully even though the Data Catalog shows only a single array column. If you need the individual column-level metadata to be available in the Data Catalog, run an AWS Glue crawler periodically to keep the AWS Glue metadata updated.

Data Lake 90

Data Lake Dashboards Metrics Metadata 90

AWS Big Data

DECEMBER 9, 2024

Icebergs branching feature Iceberg offers a branching feature for data lifecycle management, which is particularly useful for efficiently implementing the WAP pattern. The metadata of an Iceberg table stores a history of snapshots. Replace with the S3 bucket from the CloudFormation Outputs tab.

Data Quality 109

Data Quality Publishing Snapshot Data Lake 109

AWS Big Data

AUGUST 16, 2023

We fetch the metadata of the users_xxxxxx table from Athena. The following are a few important considerations regarding how the Lambda function handles Iceberg table metadata changes: In this approach, target metadata takes precedence during DML operations. It’s imperative that the source and target metadata match.

Data Lake 78

Data Lake Metadata Testing Snapshot 78

Cloudera

OCTOBER 10, 2024

The open data lakehouse is quickly becoming the standard architecture for unified multifunction analytics on large volumes of data. It combines the flexibility and scalability of data lake storage with the data analytics, data governance, and data management functionality of the data warehouse.

Optimization 84

Optimization Snapshot Data Lake Cost-Benefit 84

Cloudera

DECEMBER 3, 2024

REST Catalog Value Proposition It provides open, metastore-agnostic APIs for Iceberg metadata operations, dramatically simplifying the Iceberg client and metastore/engine integration. It provides real time metadata access by directly integrating with the Iceberg-compatible metastore. spark.sql(SELECT * FROM airlines_data.carriers).show()

Metadata 84

Metadata Data Warehouse ROI Snapshot 84

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 84

Analytics Metadata Data-driven Snapshot 84