AWS Big Data

AUGUST 1, 2024

Large-scale data warehouse migration to the cloud is a complex and challenging endeavor that many organizations undertake to modernize their data infrastructure, enhance data management capabilities, and unlock new business opportunities.

Data Warehouse 114

Data Warehouse KPI Optimization Cost-Benefit 114

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.

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Data Warehouse Testing Data Quality Reporting 126

AWS Big Data

SEPTEMBER 5, 2024

Federated queries are useful for use cases where organizations want to combine data from their operational systems with data stored in Amazon Redshift. Federated queries allow querying data across Amazon RDS for MySQL and PostgreSQL data sources without the need for extract, transform, and load (ETL) pipelines.

IoT 110

IoT Data Warehouse Cost-Benefit Reporting 110

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.

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Data Warehouse Digital Transformation Data-driven Reporting 142

AWS Big Data

NOVEMBER 29, 2023

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.

Data Lake 121

Data Lake Management Metrics Data Warehouse 121

Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

Management 99

Management Data Warehouse Digital Transformation Dashboards 99

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 114

Data Lake Data Warehouse Data-driven B2B 114

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. These upstream data sources constitute the data producer components.

Data Warehouse 110

Data Warehouse Analytics Data Lake Data Science 110

AWS Big Data

JUNE 19, 2024

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. All columns should masked for them.

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Data Warehouse Testing Sales Structured Data 114

AWS Big Data

DECEMBER 4, 2024

Central to the success of this strategy is its support for each division’s autonomy and freedom to choose their own domain structure, which is closely aligned to their business needs. These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products.

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Metadata Data Governance Data Quality Data-driven 105

AWS Big Data

DECEMBER 5, 2023

For files with known structures, a Redshift stored procedure is used, which takes the file location and table name as parameters and runs a COPY command to load the raw data into corresponding Redshift tables. She helps customers architect data analytics solutions at scale on AWS.

Measurement 105

Measurement Dashboards Data Warehouse Analytics 105

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.

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Data Lake Data Warehouse Data Architecture Metadata 88

CIO Business Intelligence

FEBRUARY 16, 2023

“Digitizing was our first stake at the table in our data journey,” he says. That step, primarily undertaken by developers and data architects, established data governance and data integration. That step, primarily undertaken by developers and data architects, established data governance and data integration.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

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 97

Enterprise Data Warehouse Reporting Metadata 97

Cloudera

AUGUST 8, 2022

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 ). Cloudera Data Engineering (Spark 3) with Airflow enabled. Cloudera Machine Learning .

Snapshot 94

Snapshot Data Warehouse Machine Learning Cost-Benefit 94

CIO Business Intelligence

APRIL 8, 2025

As data volumes and use cases scale especially with AI and real-time analytics trust must be an architectural principle, not an afterthought. Comparison of modern data architectures : Architecture Definition Strengths Weaknesses Best used when Data warehouse Centralized, structured and curated data repository.

Data Quality 102

Data Quality Data-driven Key Performance Indicator Metadata 102

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Better Data Culture.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Science Data Transformation 52

AWS Big Data

AUGUST 13, 2024

We will create a glue studio job, add events and venue data from the SFTP server, carry out data transformations and load transformed data to s3. Shengjie Luo is a Big data architect of Amazon Cloud Technology professional service team. Select Visual ETL in the central pane.

Data Processing 114

Data Processing Visualization Data Lake Data Processing 114

AWS Big Data

SEPTEMBER 19, 2023

However, our legacy data warehouse-based solution was not equipped for this challenge. It was designed to manage complex queries and business intelligence (BI) use cases on a large scale. The AWS Glue streaming jobs generate derived fields and risk profiles that get stored in Amazon DynamoDB.

Analytics 109

Analytics Risk Big Data Machine Learning 109

CIO Business Intelligence

AUGUST 3, 2023

As well as keeping its current data accurate and accessible, the company wants to leverage decades of historical data to identify potential risks to ship operations and opportunities for improvement. Each of the acquired companies had multiple data sets with different primary keys, says Hepworth. “We

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

CIO Business Intelligence

DECEMBER 22, 2022

Analytics, Collaboration Software, Data Management, Data Mining, Data Science, IT Strategy, Small and Medium Business.

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Analytics Machine Learning Data mining Software 130

Sisense

MAY 11, 2021

Here at Sisense, we think about this flow in five linear layers: Raw This is our data in its raw form within a data warehouse. We follow an ELT ( E xtract, L oad, T ransform) practice, as opposed to ETL, in which we opt to transform the data in the warehouse in the stages that follow. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

JULY 25, 2023

The integration of Talend Cloud and Talend Stitch with Amazon Redshift Serverless can help you achieve successful business outcomes without data warehouse infrastructure management. Prerequisites To complete the integration, you need a Redshift Serverless data warehouse. Add a tRedshiftOutput database component.

Data Analytics 52

Data Analytics Analytics Data Warehouse Data Processing 52

AWS Big Data

AUGUST 1, 2023

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Choose Update.

Data Lake 98

Data Lake Data Transformation Data-driven Cost-Benefit 98

IBM Big Data Hub

JANUARY 18, 2023

Data platform architecture has an interesting history. Towards the turn of millennium, enterprises started to realize that the reporting and business intelligence workload required a new solution rather than the transactional applications. A read-optimized platform that can integrate data from multiple applications emerged.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

CIO Business Intelligence

JANUARY 9, 2023

To fuel self-service analytics and provide the real-time information customers and internal stakeholders need to meet customers’ shipping requirements, the Richmond, VA-based company, which operates a fleet of more than 8,500 tractors and 34,000 trailers, has embarked on a data transformation journey to improve data integration and data management.

Data Strategy 111

Data Strategy Strategy Data Governance Marketing 111

AWS Big Data

APRIL 27, 2023

With these features, you can now build data pipelines completely in standard SQL that are serverless, more simple to build, and able to operate at scale. Typically, data transformation processes are used to perform this operation, and a final consistent view is stored in an S3 bucket or folder.

Data Lake 97

Data Lake Snapshot Optimization Data Transformation 97

Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. displaying BI insights for human users).

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

IBM Big Data Hub

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

Risk 70

Risk Modeling Management Metadata 70

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools.

Data Quality 98

Data Quality Testing Data Warehouse Unstructured Data 98

IBM Big Data Hub

JULY 17, 2023

It is supported by querying, governance, and open data formats to access and share data across the hybrid cloud. Through workload optimization across multiple query engines and storage tiers, organizations can reduce data warehouse costs by up to 50 percent.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

FineReport

APRIL 21, 2024

From addressing implementation challenges to conducting a comparative analysis of leading options, we delve into how embedded BI tools empower organizations to make informed decisions and drive business intelligence initiatives with unprecedented efficiency and precision. What Are Embedded BI Tools?

Dashboards 52

Dashboards Visualization Interactive Business Intelligence 52

IBM Big Data Hub

MARCH 14, 2024

.   Request a live demo or start a proof of concept with Amazon RDS for Db2 Db2 Warehouse SaaS on AWS The cloud-native Db2 Warehouse fulfills your price and performance objectives for mission-critical operational analytics, business intelligence (BI) and mixed workloads.

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Cost-Benefit Metadata Optimization Management 75

AWS Big Data

MARCH 3, 2023

Any time new test cases or test results are created or modified, events trigger such that processing is immediate and new snapshot files are available via an API or data is pulled at the refresh frequency of the reporting or business intelligence (BI) tool. Fixed-size data files avoid further latency due to unbound file sizes.

Software 52

Software Data Lake Testing Cost-Benefit 52

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.

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Data Warehouse Data Architecture Data Transformation Big Data 59

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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Data Lake Data Warehouse Business Intelligence Machine Learning 52