AWS Big Data

NOVEMBER 29, 2023

It enables data engineers, data scientists, and analytics engineers to define the business logic with SQL select statements and eliminates the need to write boilerplate data manipulation language (DML) and data definition language (DDL) expressions.

Data Lake 105

Data Lake Management Metrics Data Warehouse 105

AWS Big Data

MAY 30, 2023

Customers have been using data warehousing solutions to perform their traditional analytics tasks. Traditional batch ingestion and processing pipelines that involve operations such as data cleaning and joining with reference data are straightforward to create and cost-efficient to maintain. options(**additional_options).mode("append").save(s3_output_folder)

Data Lake 83

Data Lake Data Analytics Analytics Data Processing 83

AWS Big Data

MARCH 3, 2023

In this post, we share how the AWS Data Lab helped Tricentis to improve their software as a service (SaaS) Tricentis Analytics platform with insights powered by Amazon Redshift. Finally, data integrity is of paramount importance. For users that require a unified view of software quality, this is unacceptable.

Software 52

Software Data Lake Testing Cost-Benefit 52

Cloudera

SEPTEMBER 12, 2022

A data fabric answers perhaps the biggest question of all: what data do we have to work with? Managing and making individual data sources available through traditional enterprise data integration, and when end users request them, simply does not scale — especially in light of a growing number of sources and volume.

Unstructured Data 74

Unstructured Data Data Architecture Data Lake Snapshot 74

AWS Big Data

DECEMBER 13, 2023

Traditionally, customers used batch-based approaches for data movement from operational systems to analytical systems. A batch-based approach can introduce latency in data movement and reduce the value of data for analytics. usually a data warehouse) needs to reflect those changes in near real-time.

Data Warehouse 100

Data Warehouse Snapshot Data Processing Management 100

AWS Big Data

JULY 19, 2023

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

Modeling 71

Modeling Sales Data Warehouse Snapshot 71

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: 1: Multi-function analytics . Financial regulation. Reproducibility for ML Ops. Flexible and open file formats.

Metadata 90

Metadata Data Architecture Machine Learning Cost-Benefit 90

AWS Big Data

MAY 19, 2023

Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS , data warehouses ( Amazon Redshift ), search ( Amazon OpenSearch Service ), NoSQL ( Amazon DynamoDB ), machine learning ( Amazon SageMaker ), and more.

Machine Learning 85

Machine Learning Metrics Big Data Management 85

AWS Big Data

JUNE 12, 2023

Organizations across the world are increasingly relying on streaming data, and there is a growing need for real-time data analytics, considering the growing velocity and volume of data being collected. Step 6} $ REGISTRY_NAME={VAL_OF_GlueSchemaRegistryName - Ref. Step 6} $ SCHEMA_NAME={VAL_OF_SchemaName– Ref.

Management 82

Management Metadata Testing Internet of Things 82