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

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 9, 2024

Dafiti’s data infrastructure relies heavily on ETL and ELT processes, with approximately 2,500 unique processes run daily. Amazon Redshift at Dafiti Amazon Redshift is a fully managed data warehouse service, and was adopted by Dafiti in 2017. We started with 115 dc2.large We removed the DC2 cluster and completed the migration.

Data Lake 111

Data Lake Analytics Data Warehouse Data-driven 111

CIO Business Intelligence

FEBRUARY 16, 2023

The company’s orthodontics business, for instance, makes heavy use of image processing to the point that unstructured data is growing at a pace of roughly 20% to 25% per month. Advances in imaging technology present Straumann Group with the opportunity to provide its customers with new capabilities to offer their clients.

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Data Warehouse 105

datapine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. Data Quality Management Best Practices. date, month, and year).

Data Quality 241

Data Quality Metrics Data-driven Management 241

AWS Big Data

DECEMBER 4, 2024

These nodes can implement analytical platforms like data lake houses, data warehouses, or data marts, all united by producing data products. A data portal for consumers to discover data products and access associated metadata. Nodes and domains serve business needs and are not technology mandated.

Metadata 105

Metadata Data Governance Data Quality Data-driven 105

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.

Analytics 111

Analytics Dashboards Metadata Data Warehouse 111

AWS Big Data

AUGUST 6, 2024

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it straightforward and cost-effective to analyze your data. Example data The following code shows an example of raw order data from the stream: Record1: { "orderID":"101", "email":" john.

Data Warehouse 104

Data Warehouse Data-driven Modeling Internet of Things 104

DataKitchen

OCTOBER 5, 2021

Data operations (or data production) is a series of pipeline procedures that take raw data, progress through a series of processing and transformation steps, and output finished products in the form of dashboards, predictions, data warehouses or whatever the business requires. Their product is the data.

Testing 152

Testing Dashboards Measurement Experimentation 152

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. Introduction. Conclusion .

Data Processing 94

Data Processing Data Warehouse Enterprise Visualization 94

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

Sisense

MAY 11, 2021

In determining how we wanted to merge these data stacks and how we wanted to model our data, we also revisited our data strategy to consider what we as a data team were striving to achieve within the business. The right data model + artificial intelligence = augmented analytics. Dig into AI.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

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

AWS Big Data

OCTOBER 27, 2023

Efficiency : Data transformation tasks that previously took weeks or months can now be accomplished within minutes, optimizing efficiency. Prerequisites The dataset used in this solution is the NCEI/WDS Global Significant Earthquake Database , with a global listing of over 5,700 earthquakes from 2150 BC to the present.

Analytics 124

Analytics Visualization Data Integration Cost-Benefit 124

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

IBM Big Data Hub

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

Cost-Benefit 75

Cost-Benefit Metadata Optimization Management 75

FineReport

APRIL 21, 2024

However, traditional BI tools like PowerBI and Highcharts often present obstacles to sharing insights externally, leading to prolonged engagement processes and delayed decision-making. Features include interactive visualizations and native data connectors.

Dashboards 52

Dashboards Visualization Interactive Business Intelligence 52

AWS Big Data

JUNE 29, 2023

Kinesis Data Analytics for Apache Flink In our example, we perform the following actions on the streaming data: Connect to an Amazon Kinesis Data Streams data stream. View the stream data. Transform and enrich the data. Manipulate the data with Python.

Data Analytics 104

Data Analytics Analytics IoT Data Lake 104

AWS Big Data

MARCH 3, 2023

The Amazon S3 sink connector further streams data into Amazon S3 in real time by partitioning data into fixed-sized files. Fixed-size data files avoid further latency due to unbound file sizes. As a result, data was higher quality and was streamed in real time at a much faster rate. You can follow his Twitter @simongui.

Software 52

Software Data Lake Testing Cost-Benefit 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

Occam's Razor

OCTOBER 19, 2010

First presented at an eMetrics summit in 2005 the 10/90 rule was borne out of my observations of why most companies fail miserably at web analytics. If after rigorous analysis you have determined that you have evolved to a stage that you need a data warehouse then you are out of luck with Yahoo! Fourth Bit Of Context.

Analytics 136

Analytics Testing Measurement Optimization 136

Jet Global

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

Discuss, don’t present. Present your business case. To support your case, present findings from the State of Embedded Analytics study. Information Delivery The main reason software providers take on an embedded analytics project is to improve how data is presented. It is now most definitely a need-to-have.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

Jet Global

NOVEMBER 7, 2023

Add in the de facto requirement to combine all your reporting data and it presents quite a challenge. As more companies move their data into the cloud, methods for storing and managing that data also adapt and grow. This growth is caused, in part, by the increasing use of cloud platforms for data storage and processing.

Enterprise 52

Enterprise Data Warehouse Operational Reporting Reporting 52

Jet Global

OCTOBER 30, 2023

However, leveraging these insights can be challenging if your reports and data don’t speak to each other. Disconnected enterprise performance management (EPM) operational reporting can present significant limitations and challenges for your business. Transforming Financial Reporting with Dynamic Dashboards Download Now 1.

Dashboards 52

Dashboards Reporting Operational Reporting Software 52

Jet Global

MARCH 14, 2024

This allows you to fully utilize your Fabric-based systems and overcome typical obstacles related to complex data environments. Bridge Functional Gaps Fabric has shifted away from traditional relational database management systems (RDBMS), presenting users with a new challenge.

Analytics 52

Analytics Management Reporting Data Quality 52

Jet Global

MARCH 13, 2024

Complex Data Structures and Integration Processes Dynamics data structures are already complex – finance teams navigating Dynamics data frequently require IT department support to complete their routine reporting.

Reporting 52

Reporting Finance Data Quality Sales 52

BI-Survey

MARCH 6, 2025

While efficiency is a priority, data quality and security remain non-negotiable. Developing and maintaining data transformation pipelines are among the first tasks to be targeted for automation. However, caution is advised since accuracy, timeliness, and other aspects of data quality depend on the quality of data pipelines.

Data Warehouse 52

Data Warehouse Metadata Unstructured Data Data-driven 52

AWS Big Data

APRIL 28, 2025

This process helps ensure synchronization between the two systems, enabling seamless data updates for legacy systems that still rely on Hive while transitioning to Iceberg. Implementation : Synchronizing data from Iceberg tables back to Hive tables presented a different challenge.

Data Lake 69

Data Lake Metadata Cost-Benefit Snapshot 69