AWS Big Data

OCTOBER 18, 2023

To look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust set of big data tools in the AWS Cloud to support these activities. InstanceId[]' Go to the Amazon EC2 console and connect to the master node through the Session Manager. or later installed. Instances[].InstanceId[]'

Big Data 77

Big Data Data Processing Interactive Testing 77

AWS Big Data

JULY 20, 2023

Moreover, running advanced analytics and ML on disparate data sources proved challenging. To overcome these issues, Orca decided to build a data lake. By decoupling storage and compute, data lakes promote cost-effective storage and processing of big data. This ensures that the data is suitable for training purposes.

Data Lake 83

Data Lake Analytics Snapshot Optimization 83

IBM Big Data Hub

JULY 17, 2023

Additional capabilities of our ML and data science toolkit include: MLOps pipelines: Offers a collaborative studio for data scientists to build, train and deploy machine learning models with advanced features like automated machine learning and model monitoring. Later this year, it will leverage watsonx.ai

Machine Learning 69

Machine Learning Data Warehouse Modeling Cost-Benefit 69

AWS Big Data

MARCH 3, 2023

You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Let’s refer to this S3 bucket as the raw layer.

Data Lake 79

Data Lake Dashboards Metrics Metadata 79

erwin

APRIL 16, 2024

The data mesh framework In the dynamic landscape of data management, the search for agility, scalability, and efficiency has led organizations to explore new, innovative approaches. One such innovation gaining traction is the data mesh framework. This empowers individual teams to own and manage their data.

Metadata 52

Metadata Data Quality Data Governance Modeling 52

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 77

Enterprise Data Warehouse Reporting Metadata 77

Alation

JULY 18, 2022

“Leveraging the data captured by the Unity metastore, Alation will enhance our existing integration with Databricks by easily including metadata from multiple workspaces,” said Alation director of product marketing Ibby Rahmani. The Power of Partnership to Accelerate Data Transformation.

ROI 52

ROI Metadata Data Lake Digital Transformation 52

AWS Big Data

JANUARY 27, 2023

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. Apache Flink is a widely used data processing engine for scalable streaming ETL, analytics, and event-driven applications. Apache Hudi also has its own catalog management.

Data Lake 60

Data Lake Metadata Business Analysis Data-driven 60

Cloudera

NOVEMBER 22, 2021

In addition, data pipelines include more and more stages, thus making it difficult for data engineers to compile, manage, and troubleshoot those analytical workloads. As a result, alternative data integration technologies (e.g., Limited flexibility to use more complex hosting models (e.g., CRM platforms).

Data Processing 95

Data Processing Data Warehouse Enterprise Visualization 95

AWS Big Data

DECEMBER 21, 2023

To speed up the self-service analytics and foster innovation based on data, a solution was needed to provide ways to allow any team to create data products on their own in a decentralized manner. To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse.

Data Lake 85

Data Lake Data Warehouse Data-driven B2B 85

Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 1, 2023

Now, Delta managers can get a full understanding of their data for compliance purposes. Additionally, with write-back capabilities, they can clear discrepancies and input data. This also includes IT departments that develop and manage applications used by internal stakeholders and partners.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52