article thumbnail

Perform upserts in a data lake using Amazon Athena and Apache Iceberg

AWS Big Data

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance.

article thumbnail

MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

To manage the dynamism, we can resort to taking snapshots that represent immutable points in time: of models, of data, of code, and of internal state. Enter the software development layers. Versioning. ML app and software artifacts exist and evolve in a dynamic environment. For this reason, we require a strong versioning layer.

IT 360
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Use our 14-days free trial today & transform your supply chain! Welcome To The Future Of Logistics We’re on the cusp of big data transforming the nature of logistics. Big data in logistics can improve financial efficiency, provide transparency to the supply chain, and enable proactive strategic decision-making.

Big Data 275
article thumbnail

How to Use Apache Iceberg in CDP’s Open Lakehouse

Cloudera

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). We see that as of the first snapshot ( 7445571238522489274) we had data from the years 1995 to 2005 in the table.

article thumbnail

End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Every time the business requirement changes (such as adding data sources or changing data transformation logic), you make changes on the AWS Glue app stack and re-provision the stack to reflect your changes. rename_field('id', 'org_id').rename_field('name',

article thumbnail

Cloudera Data Engineering 2021 Year End Review

Cloudera

Today it’s used by many innovative technology companies at petabyte scale, allowing them to easily evolve schemas, create snapshots for time travel style queries, and perform row level updates and deletes for ACID compliance. This enabled new use-cases with customers that were using a mix of Spark and Hive to perform data transformations. .

Snapshot 118
article thumbnail

Migrate Amazon Redshift from DC2 to RA3 to accommodate increasing data volumes and analytics demands

AWS Big Data

We carried out the migration as follows: We created a new cluster with eight ra3.4xlarge nodes from the snapshot of our four-node dc2.8xlarge cluster. TB of data. We turned off our internal ETL and ELT orchestrator, to prevent our data from being updated during the migration period.

Data Lake 111