Alation

APRIL 25, 2023

But whatever your industry, perfecting your processes for making important decisions about how to handle data is crucial. Whether you deal in customer contact information, website traffic statistics, sales data, or some other type of valuable information, you’ll need to put a framework of policies in place to manage your data seamlessly.

Data Governance 40

Data Governance ETL Natural Language Processing Machine Learning 40

Data Science Dojo

SEPTEMBER 6, 2023

Data Security: A Multi-layered Approach In data warehousing, data security is not a single barrier but a well-constructed series of layers, each contributing to protecting valuable information. Data ownership extends beyond mere possession—it involves accountability for data quality, accuracy, and appropriate use.

Data Warehouse 195

Data Warehouse Data Governance Data Quality ETL 195

Smart Data Collective

DECEMBER 28, 2022

Monitor Your Data Sources. Data sources can be the most unpredictable part of a data pipeline. It’s essential to keep an eye on them and ensure they send valid data. For example, collect customer information from a satisfaction survey. Data accuracy can be managed manually or with automated tools.

Data Pipeline 119

Data Pipeline Data Governance ETL Big Data 119

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

OCTOBER 19, 2023

Hence, the quality of data is significant here. Quality data fuels business decisions, informs scientific research, drives technological innovations, and shapes our understanding of the world. The Relevance of Data Quality Data quality refers to the accuracy, completeness, consistency, and reliability of data.

Data Quality 52

Data Quality Data Governance Data Warehouse Machine Learning 52

Dataversity

NOVEMBER 15, 2023

Those who want to design universal data pipelines and ETL testing tools face a tough challenge because of the vastness and variety of technologies: Each data pipeline platform embodies a unique philosophy, architectural design, and set of operations.

Data Pipeline 59

Data Pipeline ETL Data Governance Data Quality 59

IBM Data Science in Practice

NOVEMBER 28, 2022

IBM’s Next Generation DataStage is an ETL tool to build data pipelines and automate the effort in data cleansing, integration and preparation. As a part of data pipeline, Address Verification Interface (AVI) can remediate bad address data.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

phData

AUGUST 9, 2024

Typically, this data is scattered across Excel files on business users’ desktops. They usually operate outside any data governance structure; often, no documentation exists outside the user’s mind. Sales team demographic information is pulled from the CRM. Financial data is pulled from the ERP.

ETL 52

ETL Data Warehouse Data Governance Azure 52

phData

JUNE 23, 2023

In the world of data-driven organizations, understanding the origin, movement, and transformations of data is paramount. Data lineage plays a crucial role in providing transparency, ensuring data integrity, and enabling informed decision-making. What is Data Lineage?

Data Quality 52

Data Quality Data Governance ETL Database 52

Dataconomy

SEPTEMBER 4, 2023

Cloud analytics is the art and science of mining insights from data stored in cloud-based platforms. By tapping into the power of cloud technology, organizations can efficiently analyze large datasets, uncover hidden patterns, predict future trends, and make informed decisions to drive their businesses forward.

Analytics 203

Analytics Analytics Big Data Analytics Big Data Analytics 203

IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times.

Database 97

Database Data Engineering Data Engineer Data Engineering 97

Pickl AI

NOVEMBER 15, 2023

This flexibility allows organizations to store vast amounts of raw data without the need for extensive preprocessing, providing a comprehensive view of information. Centralized Data Repository Data Lakes serve as a centralized repository, consolidating data from different sources within an organization.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Data Science Dojo

OCTOBER 31, 2024

Specialized Industry Knowledge The University of California, Berkeley notes that remote data scientists often work with clients across diverse industries. Whether it’s finance, healthcare, or tech, each sector has unique data requirements.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

AWS Machine Learning Blog

NOVEMBER 12, 2024

Focused on addressing the challenge of agricultural data standardization, Agmatix has developed proprietary patented technology to harmonize and standardize data, facilitating informed decision-making in agriculture. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

AWS 104

AWS AI AI Data Lakes 104

IBM Journey to AI blog

JUNE 29, 2023

IBP brings together various functions, including sales, marketing, finance, supply chain, human resources, IT and beyond to collaborate across business units and make informed decisions that drive overall business success. Data integration and analytics IBP relies on the integration of data from different sources and systems.

Business Intelligence 98

Business Intelligence Business Intelligence Analytics Analytics 98

Pickl AI

OCTOBER 10, 2024

Support for Advanced Analytics : Transformed data is ready for use in Advanced Analytics, Machine Learning, and Business Intelligence applications, driving better decision-making. Compliance and Governance : Many tools have built-in features that ensure data adheres to regulatory requirements, maintaining data governance across organisations.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Dataversity

DECEMBER 14, 2021

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

Data Governance 52

Data Governance Data Observability ETL Data Quality 52

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

ODSC - Open Data Science

JUNE 12, 2023

Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 64

Data Governance ETL Data Pipeline Azure 64

Dataversity

SEPTEMBER 21, 2021

In my first business intelligence endeavors, there were data normalization issues; in my Data Governance period, Data Quality and proactive Metadata Management were the critical points. The post The Declarative Approach in a Data Playground appeared first on DATAVERSITY. It is something so simple and so powerful.

Data Governance 98

Data Governance Business Intelligence Business Intelligence Data Quality 98

Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

Big Data 110

Big Data Big Data Data Engineering Data Engineer 110

Alation

MAY 16, 2023

Many organizations use data visualization to identify patterns or consumer trends and communicate findings to stakeholders better. Data Integration A data pipeline can be used to gather data from various disparate sources in one data store. Checking the data quality before and after the cleansing steps is critical.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

Pickl AI

JULY 30, 2024

With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration. Overview In the era of Big Data , organizations inundated with vast amounts of information generated from various sources. Apache NiFi is used for automating the flow of data between systems.

ETL 52

ETL Data Lakes Big Data Big Data 52

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

Pickl AI

JULY 3, 2023

Business Intelligence (BI) refers to the technology, techniques, and practises that are used to gather, evaluate, and present information about an organisation in order to assist decision-making and generate effective administrative action. Based on the report of Zion Research, the global market of Business Intelligence rose from $16.33

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Pickl AI

AUGUST 11, 2024

Additionally, it addresses common challenges and offers practical solutions to ensure that fact tables are structured for optimal data quality and analytical performance. Introduction In today’s data-driven landscape, organisations are increasingly reliant on Data Analytics to inform decision-making and drive business strategies.

Data Quality 52

Data Quality Data Warehouse Data Governance Business Intelligence 52

phData

APRIL 18, 2023

Data Clean Rooms provide data exchange, double-blind joins, and limited searches, resulting in several firms exchanging and matching consumer data without disclosing any underlying information. Once that data is tagged, an access history log is available as required for regulatory compliance.

Data Silos 52

Data Silos ETL Clustering Data Warehouse 52

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. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

The Data Administration Newsletter

MARCH 20, 2024

Organizations often struggle with finding nuggets of information buried within their data to achieve their business goals. Technology sometimes comes along to offer some interesting solutions that can bridge that gap for teams that practice good data management hygiene.

ETL 105

ETL Data Governance Big Data Big Data 105

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Data lakes are often used for situations in which an organization wishes to store information for possible future use.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Apache Hadoop 59

The MLOps Blog

MARCH 15, 2023

Data governance: Ensure that the data used to train and test the model, as well as any new data used for prediction, is properly governed. For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial.

AWS 52

AWS ETL ML ML 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Schema Enforcement: Data warehouses use a “schema-on-write” approach. Data must be transformed and structured before loading, ensuring data consistency and quality.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

phData

AUGUST 17, 2023

They offer a range of features and integrations, so the choice depends on factors like the complexity of your data pipeline, requirements for connections to other services, user interface, and compatibility with any ETL software already in use. This enhances the reliability and resilience of the data pipeline.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Precisely

MAY 31, 2023

Business and IT teams need a simple, seamless data integrity solution that enables them to: Collaborate on making data accessible Ensure and maintain its quality Enrich the data with third-party context Real-time Access to Data In the past, businesses struggled to get timely access to data for both analytical and operational use cases.

Data Quality 52

Data Quality Data Governance Data Observability ETL 52

IBM Journey to AI blog

JULY 1, 2024

Data lineage is the discipline of understanding how data flows through your organization: where it comes from, where it goes, and what happens to it along the way. Often used in support of regulatory compliance, data governance and technical impact analysis, data lineage answers these questions and more.

ETL 40

ETL Data Lakes Database Data Pipeline 40