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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. To overcome these issues, Orca decided to build a data lake.

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

AWS Big Data

Apache Iceberg is an open table format for data lakes that manages large collections of files as tables. It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. However, this requires knowledge of a table’s current snapshots.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Organizations with legacy, on-premises, near-real-time analytics solutions typically rely on self-managed relational databases as their data store for analytics workloads. Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities.

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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.

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

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.

Big Data 275
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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',

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

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.

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