AWS Big Data

NOVEMBER 27, 2024

Together with price-performance, Amazon Redshift offers capabilities such as serverless architecture, machine learning integration within your data warehouse and secure data sharing across the organization. dbt Cloud is a hosted service that helps data teams productionize dbt deployments.

Data Warehouse 110

Data Warehouse Analytics Testing Modeling 110

AWS Big Data

JANUARY 30, 2025

Pattern 1: Data transformation, load, and unload Several of our data pipelines included significant data transformation steps, which were primarily performed through SQL statements executed by Amazon Redshift. The following Diagram 2 shows this workflow.

Data Warehouse 54

Data Warehouse Data Architecture Machine Learning Data Transformation 54

AWS Big Data

JANUARY 6, 2025

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

Analytics 98

Analytics Data Warehouse Metrics Big Data 98

erwin

SEPTEMBER 17, 2020

With complex data architectures and systems within so many organizations, tracking data in motion and data at rest is daunting to say the least. Harvesting the data through automation seamlessly removes ambiguity and speeds up the processing time-to-market capabilities.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

Cloudera

JUNE 30, 2022

These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data.

Data Lake 88

Data Lake Data Warehouse Data Architecture Metadata 88

AWS Big Data

NOVEMBER 9, 2023

In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

Data Warehouse 126

Data Warehouse Testing Data Quality Reporting 126

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 115

Data Warehouse KPI Optimization Cost-Benefit 115

AWS Big Data

NOVEMBER 15, 2023

The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.

Dashboards 111

Dashboards Analytics Metadata Data Warehouse 111

Cloudera

DECEMBER 21, 2021

We are excited to offer in Tech Preview this born-in-the-cloud table format that will help future proof data architectures at many of our public cloud customers. This enabled new use-cases with customers that were using a mix of Spark and Hive to perform data transformations. . Modernizing pipelines.

Snapshot 118

Snapshot Data-driven Optimization Management 118

AWS Big Data

APRIL 19, 2023

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

Technology 105

Technology Data-driven Machine Learning Sales 105

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

The former allows us to control the data before it is generated, and the latter allows us to identify if there is an issue with our data that would impact its availability, completeness, or accuracy. Process-driven data integrity: Getting data generation right. Cleaning up data that doesn’t meet data integrity standards.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

Cloudera

NOVEMBER 29, 2021

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The company needed a modern data architecture to manage the growing traffic effectively. .

Data Lake 77

Data Lake Data Warehouse Data Architecture Reporting 77

AWS Big Data

FEBRUARY 6, 2023

He mainly works with enterprise customers to help data lake migration and modernization, and provides guidance and technical assistance on big data projects such as Hadoop, Spark, data warehousing, real-time data processing, and large-scale machine learning. George Zhao is a Senior Data Architect at AWS ProServe.

Dashboards 98

Dashboards Optimization Data Lake Cost-Benefit 98

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. This ensures that the data is suitable for training purposes. These robust capabilities ensure that data within the data lake remains accurate, consistent, and reliable.

Data Lake 98

Data Lake Analytics Snapshot Data Quality 98

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

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

Enterprise 111

Enterprise Data Warehouse Data Lake Optimization 111

Peter James Thomas

MARCH 9, 2020

Much as Einstein may have said that everything should be as simple as possible, but no simpler [3] , Data Maturity should be as great as necessary, but no greater; over-engineering has been the downfall of many a Data Transformation Programme.

Measurement 67

Measurement Data Strategy Strategy Modeling 67

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

Ontotext

MARCH 27, 2024

It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards. LPGs are rudimentary knowledge graphs.

Modeling 53

Modeling Metadata Data Quality Enterprise 53

Peter James Thomas

OCTOBER 30, 2018

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. It may be to build a new (or a first) Data Architecture. It may be to introduce or expand Data Governance.

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

He mainly works with enterprise customers to help data lake migration and modernization, and provides guidance and technical assistance on big data projects such as Hadoop, Spark, data warehousing, real-time data processing, and large-scale machine learning. George Zhao is a Senior Data Architect at AWS ProServe.

Cost-Benefit 52

Cost-Benefit Data Lake Dashboards Big Data 52

IBM Big Data Hub

JANUARY 18, 2023

This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. The data warehouse storage layer is removed from lakehouse architectures. Instead, continuous data transformation is performed within the BLOB storage. Data mesh: A mostly new culture.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

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. Data transformation – Steps 3 and 4 represent an EMR Serverless Spark application (Amazon EMR 6.9 Monjumi Sarma is a Data Lab Solutions Architect at AWS.

Data Lake 98

Data Lake Dashboards Metrics Metadata 98

TDAN

DECEMBER 3, 2019

Everyone’s talking about data. Data is the key to unlocking insight— the secret sauce that will help you get predictive, the fuel for business intelligence. The transformative potential in AI? It relies on data. The good news is that data has never […].

Business Intelligence 72

Business Intelligence Dashboards Reporting Data Architecture 72

Data Virtualization

JANUARY 20, 2021

Learn in 12 minutes: What makes a strong use case for data virtualisation How to come up with a solid Proof of Concept How to prepare your organisation for data virtualisation You’ll have read all about data virtualisation and you’ve.

Data Warehouse 59

Data Warehouse Data Architecture Data Transformation Big Data 59

Data Virtualization

APRIL 21, 2021

BHP is a global resources company headquartered in Melbourne, Australia. It is among the world’s top producers of major commodities, including iron ore, metallurgical coal, and copper, and has substantial interests in oil and gas. BHP has operations and offices.

IT 52

IT Data-driven Data Architecture Data Transformation 52

CIO Business Intelligence

AUGUST 3, 2023

At Paytronix, which manages customer loyalty, online ordering, and other systems for its customers, director of data science ­Jesse Marshall wanted to reduce the custom coding of data transformations—the conversion, cleaning, and structuring of data into a form usable for analytics and reports.

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

AWS Big Data

DECEMBER 21, 2023

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).

Data Lake 115

Data Lake Data Warehouse Data-driven B2B 115

CIO Business Intelligence

AUGUST 7, 2024

The company started its New Analytics Era initiative by migrating its data from outdated SQL servers to a modern AWS data lake. It then built a cutting-edge cloud-based analytics platform, designed with an innovative data architecture. It also crafted multiple machine learning and AI models to tackle business challenges.

IT 119

IT Insurance Cost-Benefit Testing 119

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

CIO Business Intelligence

NOVEMBER 7, 2023

Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever. The aim is to normalize, aggregate, and eventually make available to analysts across the organization data that originates in various pockets of the enterprise.

Data Warehouse 142

Data Warehouse Digital Transformation Data-driven Reporting 142

Ontotext

FEBRUARY 12, 2024

For many organizations, a centralized data platform will fall short as it gives data teams much less autonomy over managing increasingly diverse and voluminous datasets. A centralized data engineering team focuses on building a governed self-serviced infrastructure, while domain teams use the services to build full-stack data products.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

FineReport

MARCH 31, 2024

Furthermore, these tools boast customization options, allowing users to tailor data sources to address areas critical to their business success, thereby generating actionable insights and customizable reports. Best BI Tools for Data Analysts 3.1 Key Features: Extensive library of pre-built connectors for diverse data sources.

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Birst BI

FEBRUARY 25, 2020

Customers such as Crossmark , DJO Global and others use Birst with Snowflake to deliver the ultimate modern data architecture. Data never leaves Snowflake with Birst’s ability to support the reporting and self-service needs of both centralized IT and decentralized LOB teams.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60