erwin

SEPTEMBER 17, 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.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

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. And there’s control of that landscape to facilitate insight and collaboration and limit risk.

Data Governance 40

Data Governance Risk Metadata Management 40

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. With NiFi you can configure your source processor and run it independently of any other processors to retrieve data. Enabling self-service for developers.

Testing 95

Testing Cost-Benefit Interactive Visualization 95

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

AWS Big Data

JANUARY 17, 2024

Incremental query refers to a query strategy that focuses on processing and analyzing only the new or updated data within a data lake since the last query. The key idea behind incremental queries is to use metadata or change tracking mechanisms to identify the new or modified data since the last query.

Data Lake 89

Data Lake Snapshot Big Data Data-driven 89

IBM Big Data Hub

JANUARY 9, 2024

The entire generative AI pipeline hinges on the data pipelines that empower it, making it imperative to take the correct precautions. 4 key components to ensure reliable data ingestion Data quality and governance: Data quality means ensuring the security of data sources, maintaining holistic data and providing clear metadata.

Enterprise 83

Enterprise Data Integration Data Quality Contextual Data 83

Alation

AUGUST 11, 2022

We chatted about industry trends, why decentralization has become a hot topic in the data world, and how metadata drives many data-centric use cases. But, through it all, Mohan says it’s critical to view everything through the same lens: gaining business value from data. Data fabric is a technology architecture.

Metadata 52

Metadata Data Warehouse Data Quality Data Lake 52

Alation

APRIL 4, 2023

Now, joint users will get an enhanced view into cloud and data transformations , with valuable context to guide smarter usage. Integrating helpful metadata into user workflows gives all people, from data scientists to analysts , the context they need to use data more effectively.

Metadata 52

Metadata Cost-Benefit Data Transformation Predictive Modeling 52

Cloudera

OCTOBER 7, 2022

We’re excited to announce the general availability of the open source adapters for dbt for all the engines in CDP — Apache Hive , Apache Impala , and Apache Spark, with added support for Apache Livy and Cloudera Data Engineering. The Open Data Lakehouse . Cloudera builds dbt adaptors for all engines in the open data lakehouse.

Data Warehouse 79

Data Warehouse Data Transformation Testing Data Lake 79

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

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 fabric promotes data discoverability.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52

IBM Big Data Hub

MARCH 14, 2024

This involves unifying and sharing a single copy of data and metadata across IBM® watsonx.data ™, IBM® Db2 ®, IBM® Db2® Warehouse and IBM® Netezza ®, using native integrations and supporting open formats, all without the need for migration or recataloging.

Cost-Benefit 87

Cost-Benefit Metadata Optimization Management 87

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 83

Data Lake Data Architecture Metadata Data Warehouse 83

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 Business Intelligence 52

IBM Big Data Hub

JULY 17, 2023

In this blog, I will cover: What is watsonx.ai? Capabilities within the Prompt Lab include: Summarize: Transform text with domain-specific content into personalized overviews and capture key points (e.g., foundation models to help users discover, augment, and enrich data with natural language. What is watsonx.data?

Machine Learning 70

Machine Learning Data Warehouse Modeling Cost-Benefit 70

erwin

MARCH 4, 2021

In addition to drivers like digital transformation and compliance, it’s really important to look at the effect of poor data on enterprise efficiency/productivity. Then it is accessible and understandable via role-based, contextual views so stakeholders can make strategic decisions based on accurate insights.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

AWS Big Data

JULY 20, 2023

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

Data Lake 84

Data Lake Analytics Snapshot Optimization 84

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 For Name , enter emr-delta-blog. For Type , choose Spark.

Data Lake 79

Data Lake Dashboards Metrics Metadata 79

erwin

APRIL 16, 2024

The data mesh approach distributes data ownership and decentralizes data architecture, paving the way for enhanced agility and scalability. With distributed ownership there is a need for effective governance to ensure the success of any data initiative. Business Glossaries – what is the business meaning of our data?

Metadata 52

Metadata Data Quality Data Governance Modeling 52

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 99

Data Lake Management Metrics Data Warehouse 99

Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. This is something that you can learn more about in just about any technology blog.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

Cloudera

OCTOBER 11, 2021

The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.

Data Lake 89

Data Lake Cost-Benefit Data-driven Dashboards 89

AWS Big Data

AUGUST 1, 2023

Click Launch Stack : For Stack name , enter a name for the stack (the default is aws-blog-jira-datalake-with-AppFlow ). For GlueDatabaseName , enter a unique name for the Data Catalog database to hold the Jira data table metadata (the default is jiralake ). Complete the following steps: Sign in to your AWS account.

Data Lake 81

Data Lake Data Transformation Cost-Benefit Data-driven 81

Alation

NOVEMBER 22, 2022

Before you implement a data governance framework, you need to know the data you already have. This means you need to: Inventory data: Know all information resources and relevant metadata. Classify data: Organize structured and unstructured data into relevant categories. Reuse metadata productively.

Data Governance 52

Data Governance Metadata Data-driven Unstructured Data 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. Comprehensive data security and data governance (i.e.

Data Lake 70

Data Lake Metadata Cost-Benefit Data Warehouse 70

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. Netflix implemented this without domain users knowing the underlying technologies and complexity.

Data-driven 59

Data-driven Data Lake Data Quality Business Objectives 59

DataKitchen

JANUARY 23, 2023

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

Testing 130

Testing Data Governance Data Quality Data-driven 130

Cloudera

NOVEMBER 22, 2021

In addition, more data is becoming available for processing / enrichment of existing and new use cases e.g., recently we have experienced a rapid growth in data collection at the edge and an increase in availability of frameworks for processing that data. As a result, alternative data integration technologies (e.g.,

Data Processing 88

Data Processing Data Warehouse Enterprise Visualization 88