Alation

OCTOBER 27, 2022

Few actors in the modern data stack have inspired the enthusiasm and fervent support as dbt. This data transformation tool enables data analysts and engineers to transform, test and document data in the cloud data warehouse. But what does this mean from a practitioner perspective?

Metrics 52

Metrics Dashboards Sales Reporting 52

Cloudera

JUNE 1, 2021

These acquisitions usher in a new era of “ self-service ” by automating complex operations so customers can focus on building great data-driven apps instead of managing infrastructure. Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS.

Uncertainty 144

Uncertainty Cost-Benefit Risk Strategy 144

AWS Big Data

APRIL 30, 2024

Amazon Q Developer can now generate complex data integration jobs with multiple sources, destinations, and data transformations. Generated jobs can use a variety of data transformations, including filter, project, union, join, and custom user-supplied SQL.

Data Integration 103

Data Integration Data Lake Data Warehouse Software 103

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

datapine

SEPTEMBER 29, 2022

Data processes that depended upon the previously defective data will likely need to be re-initiated, especially if their functioning was at risk or compromised by the defected data. These processes could include reports, campaigns, or financial documentation. Accuracy should be measured through source documentation (i.e.,

Data Quality 241

Data Quality Metrics Data-driven Management 241

IBM Big Data Hub

NOVEMBER 20, 2023

Today’s healthcare providers use a wide variety of applications and data across a broad ecosystem of partners to manage their daily workflows. Integrating these applications and data is critical to their success, allowing them to deliver patient care efficiently and effectively.

Cost-Benefit 89

Cost-Benefit Data-driven Data Transformation Management 89

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

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 135

Data Warehouse Digital Transformation Data-driven Reporting 135

erwin

APRIL 29, 2020

Business terms and data policies should be implemented through standardized and documented business rules. Compliance with these business rules can be tracked through data lineage, incorporating auditability and validation controls across data transformations and pipelines to generate alerts when there are non-compliant data instances.

Metadata 111

Metadata Data Governance Key Performance Indicator Data Quality 111

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence. Track models and drive transparent processes.

Risk 71

Risk Modeling Management Metadata 71

AWS Big Data

JULY 25, 2023

As of this writing, Talend has 14 distinct native connectivity and configuration components for Amazon Redshift, which are fully documented in the Talend Help Center. As well as Talend Cloud for enterprise-level data transformation needs, you could also use Talend Stitch to handle data ingestion and data replication to Redshift Serverless.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

Smart Data Collective

APRIL 29, 2020

The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation.

Management 83

Management Data Warehouse Digital Transformation Dashboards 83

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on.

Data Governance 40

Data Governance Risk Metadata Management 40

Cloudera

AUGUST 3, 2022

By leveraging Hive to apply Ranger FGAC, Spark obtains secure access to the data in a protected staging area. Since Spark has direct access to the staged data, any Spark APIs can be used, from complex data transformations to data science and machine learning. . so stay tuned! .

Snapshot 88

Snapshot Cost-Benefit Machine Learning Data Transformation 88

Alation

OCTOBER 18, 2022

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Yet every dbt transformation contains vital metadata that is not captured – until now. Data Transformation in the Modern Data Stack. How did the data transform exactly?

Metadata 52

Metadata Metrics Recreation/Entertainment Data Quality 52

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.

Data Governance 76

Data Governance Business Objectives Strategy Data Quality 76

Sisense

AUGUST 6, 2020

Take Grammarly as an example: This popular program checks the grammar, tone, and style of documents. Getting this AI properly trained required a huge learning dataset with countless documents that were tagged according to specific criteria. Accurately prepared data is the base of AI. What will it take to build your MVP?

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Alation

JUNE 28, 2022

A metadata management framework combines organizational structure and a set of tools to create a data asset taxonomy. Document type: describes creation, storage, and use during business processes. Scale effectively: Leverage taxonomies to ensure consistent modeling outcomes when introducing new data sets or changing business demands.

Metadata 52

Metadata Management Data Governance Machine Learning 52

Jet Global

MARCH 14, 2024

This straightforward and user-friendly access to source data makes it easier for your business users to examine and extract insights from your core data systems. Data Lineage and Documentation Jet Analytics simplifies the process of documenting data assets and tracking data lineage in Fabric.

Analytics 52

Analytics Management Reporting Enterprise 52

Octopai

OCTOBER 6, 2022

It all happens through the magic of data transformation. Data transformation can include: Aggregation Discretization Generalization Conversion Normalization Filtering Smoothing. Identify which transformation process is needed to take your data from the source structure to the target structure.

Testing 52

Testing Visualization Data Transformation Strategy 52

CDW Research Hub

OCTOBER 25, 2019

As we review data transformation and modernization strategies with our clients, we find many are investigating Snowflake as a data warehouse solution due to its ease of use, speed, and increased flexibility over a traditional data warehouse offering. Mapping a successful data migration can bring on rough weather.

Data Warehouse 40

Data Warehouse Testing Strategy Data-driven 40

Alation

JANUARY 17, 2023

These help data analysts visualize key insights that can help you make better data-backed decisions. ELT Data Transformation Tools: ELT data transformation tools are used to extract, load, and transform your data. Examples of data transformation tools include dbt and dataform.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

erwin

MARCH 26, 2020

Increased data variety, balancing structured, semi-structured and unstructured data, as well as data originating from a widening array of external sources. Reducing the IT bottleneck that creates barriers to data accessibility. Hybrid on-premises/cloud environments that complicate data integration and preparation.

Data-driven 90

Data-driven Unstructured Data Enterprise Finance 90

AWS Big Data

OCTOBER 5, 2023

With Amazon AppFlow, you can run data flows at nearly any scale at the frequency you choose—on 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.

Data Warehouse 86

Data Warehouse Machine Learning Data Integration Data-driven 86

AWS Big Data

MARCH 13, 2024

You can also use the data transformation feature of Data Firehose to invoke a Lambda function to perform data transformation in batches. This method uses GZIP compression to optimize storage consumption and query performance.

Analytics 95

Analytics IoT Metadata Internet of Things 95

O'Reilly on Data

JANUARY 17, 2023

Instead of invoking the open-source scikit-learn or Keras calls to build models, your team now goes from Pandas data transforms straight to … the API calls for AWS AutoPilot or GCP Vertex AI. It does not exist in the code. AutoML drives this point home. And it’s available to everyone. What if we go the other way?

Machine Learning 321

Machine Learning Predictive Modeling Software Modeling 321

Cloudera

DECEMBER 9, 2022

Developers need to onboard new data sources, chain multiple data transformation steps together, and explore data as it travels through the flow. Now, we shift focus on the needs of developers and addressing the challenges they face when building dataflows in the cloud. Enabling self-service for developers.

Testing 98

Testing Cost-Benefit Interactive Visualization 98

DataRobot Blog

MARCH 10, 2022

The first step in building a model that can predict machine failure and even recommend the next best course of action is to aggregate, clean, and prepare data to train against. This task may require complex joins, aggregations, filtering, window functions, and many other data transformations against extremely large-scale data sets.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

AWS Big Data

OCTOBER 31, 2023

Configure access permissions for the S3 bucket While creating the IAM policy, a sample policy document code is needed (see the following code), which provides Snowflake with the required permissions to load or unload data using a single bucket and folder path. On the IAM console, choose Roles in the navigation pane.

Data Processing 90

Data Processing Management Publishing Testing 90

AWS Big Data

APRIL 19, 2023

This allows for a new way of thinking and new organizational elements—namely, a modern data community. However, today’s data mesh platform contains largely independent data products. Even with well-documented data products, knowing how to connect or join data products is a time-consuming job.

Technology 83

Technology Data-driven Machine Learning Sales 83

AWS Big Data

JULY 6, 2023

It has not been specifically designed for heavy data transformation tasks. Additionally, check out the official documentation of AWS Glue , Lambda , and Step Functions. You also use AWS Glue to consolidate the files produced by the parallel tasks. Note that Lambda is a general purpose serverless engine.

Machine Learning 130

Machine Learning Visualization IoT Digital Transformation 130

AWS Big Data

MARCH 9, 2023

This can be done using the initiatePrint action: embeddedDashboard.initiatePrint(); The following code sample shows a loading animation, SDK code status, and dashboard interaction monitoring, along with initiating dashboard print from the application: Embedding demo $(document).ready(function()

Slice and Dice 81

Slice and Dice Dashboards Analytics Interactive 81

IBM Big Data Hub

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

Machine Learning 62

Machine Learning Data Warehouse Modeling Cost-Benefit 62

AWS Big Data

OCTOBER 27, 2023

Efficiency : Data transformation tasks that previously took weeks or months can now be accomplished within minutes, optimizing efficiency. Follow the documentation to clean up the Google resources. Cost efficiency : Building and maintaining custom connectors can be expensive.

Analytics 96

Analytics Visualization Data Integration Cost-Benefit 96

Jet Global

AUGUST 9, 2023

Process Runner GLSU and Wands for SAP provide flexible, intuitive interfaces for SAP data entry and transaction posting directly from Microsoft Excel. Automate financial document posting processes resulting in a shorter month-end close. Increase data accuracy and improve audit processing while running a stress-free finance operation.

Finance 52

Finance Reporting Management Enterprise 52

Cloudera

MARCH 14, 2023

A critical feature for every developer however is to get instantaneous feedback like configuration validations or performance metrics, as well as previewing data transformations for each step of their data flow. We are excited to hear your feedback and we hope you will enjoy building your data flows with the new Designer.

Testing 83

Testing Publishing Metadata Interactive 83

bridgei2i

SEPTEMBER 3, 2020

Ronobijay: Sure, I think it would, you know, what used to be anathema till a few months back, you know, data transformation is real now, right? We would have to visit a branch possibly, you know, multiple locations, submit multiple documents. So earlier customers would spend a week or two, trying to open a bank account.

Enterprise 94

Enterprise Insurance Digital Transformation Interactive 94