AWS Big Data

NOVEMBER 16, 2023

Data Vault 2.0 allows for the following: Agile data warehouse development Parallel data ingestion A scalable approach to handle multiple data sources even on the same entity A high level of automation Historization Full lineage support However, Data Vault 2.0 JOB_NAME All The process name from the ETL framework.

Enterprise 110

Enterprise Data Warehouse Data Lake Optimization 110

AWS Big Data

SEPTEMBER 5, 2024

If storing operational data in a data warehouse is a requirement, synchronization of tables between operational data stores and Amazon Redshift tables is supported. In scenarios where data transformation is required, you can use Redshift stored procedures to modify data in Redshift tables.

IoT 111

IoT Data Warehouse Cost-Benefit Reporting 111

Sisense

AUGUST 28, 2020

All these pitfalls are avoidable with the right data integrity policies in place. Means of ensuring data integrity. Data integrity can be divided into two areas: physical and logical. Physical data integrity refers to how data is stored and accessed. How are your devices physically secured?

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

AWS Big Data

AUGUST 1, 2024

However, you might face significant challenges when planning for a large-scale data warehouse migration. The following diagram illustrates a scalable migration pattern for extract, transform, and load (ETL) scenario. The success criteria are the key performance indicators (KPIs) for each component of the data workflow.

Data Warehouse 113

Data Warehouse KPI Optimization Cost-Benefit 113

IBM Big Data Hub

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture. Don’t try to do everything at once!

Data Governance 76

Data Governance Business Objectives Data Quality Measurement 76

AWS Big Data

NOVEMBER 13, 2023

Key considerations Gameskraft embraces a modern data architecture, with the data lake residing in Amazon S3. To grant seamless access to the data lake, we use the innovative capabilities of Redshift Spectrum, which is a bridge between the data warehouse (Amazon Redshift) and data lake (Amazon S3).

Data Warehouse 111

Data Warehouse Analytics Data Lake Data Science 111

AWS Big Data

MARCH 3, 2023

Data ingestion – Steps 1 and 2 use AWS DMS, which connects to the source database and moves full and incremental data (CDC) to Amazon S3 in Parquet format. Let’s refer to this S3 bucket as the raw layer. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

Peter James Thomas

MARCH 9, 2020

I used to talk about carrying out a Situational Analysis of Data Capabilities, nowadays I am more likely to refer to a Data Capability Review. I make such reviews with respect to my own Data Capability Framework, which I introduced to the public in 2019 via A Simple Data Capability Framework.

Measurement 67

Measurement Data Strategy Strategy Modeling 67

Peter James Thomas

OCTOBER 30, 2018

All the references I can find to it are modern pieces comparing it to the CDO role, so perhaps it is apochryphal. It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

Anmut

MARCH 24, 2022

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success. It doesn’t have to be this way.

ROI 52

ROI Measurement Data-driven Data Transformation 52

AWS Big Data

FEBRUARY 6, 2023

To dive deep into the HMDK TCO tool, refer to the next post in this series, How AWS ProServe Hadoop TCO tool accelerate Hadoop workload migrations to Amazon EMR. He also understands how to apply technologies to solve big data problems and build a well-designed data architecture.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

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

AWS Big Data

NOVEMBER 14, 2024

We use the built-in features of Data Firehose, including AWS Lambda for necessary data transformation and Amazon Simple Notification Service (Amazon SNS) for near real-time alerts. For more information, refer to Amazon Kinesis Data Firehose now supports zero buffering. Munim Abbasi is currently a Sr.

Data Lake 105

Data Lake Metadata Testing Data-driven 105

AWS Big Data

DECEMBER 2, 2024

AWS Glue establishes a secure connection to HubSpot using OAuth for authorization and TLS for data encryption in transit. AWS Glue also supports the ability to apply complex data transformations, enabling efficient data integration and preparation to meet your needs. For more information on AWS Glue, visit AWS Glue.

Data Lake 103

Data Lake Testing Data Integration Metadata 103

DataKitchen

JANUARY 23, 2023

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more. Are problems with data tests?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Jet Global

MAY 1, 2023

that gathers data from many sources. Data Environment First off, the solutions you consider should be compatible with your current data architecture. We have outlined the requirements that most providers ask for: Data Sources Strategic Objective Use native connectivity optimized for the data source.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52