DataKitchen

MAY 10, 2024

Critical Questions for Data Ingestion Monitoring Effective data ingestion anomaly monitoring should address several critical questions to ensure data integrity: Are there any unknown anomalies affecting the data? Have all the source files/data arrived on time? Is the source data of expected quality?

Data Ingestion 52

Data Ingestion Transportation High Quality Data Data 52

Knowledge Hut

APRIL 23, 2024

While data warehouses focus on structured data for historical analysis, big data platforms enable processing and analysis of diverse, large-scale, and often unstructured data in real-time. The focus is on maintaining a historical record of data, ensuring data integrity and consistency for reporting and analysis purposes.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Towards Data Science

NOVEMBER 4, 2023

Introduction to Apache Iceberg Tables Simplified data integrations Managed solutions like Fivetran and Stitch were built to manage third-party API integrations with ease. These days many companies choose this approach to simplify data interactions with their external data sources.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

AltexSoft

OCTOBER 3, 2022

The logical basis of RDF is extended by related standards RDFS (RDF Schema) and OWL (Web Ontology Language). They allow for representing various types of data and content (data schema, taxonomies, vocabularies, and metadata) and making them understandable for computing systems. General scenarios of using knowledge graphs.

Relational Database 52

Relational Database Banking Media Computer Science 52

The Modern Data Company

MAY 8, 2023

And by leveraging distributed storage and open-source technologies, they offer a cost-effective solution for handling large data volumes. In other words, the data is stored in its raw, unprocessed form, and the structure is imposed when a user or an application queries the data for analysis or processing.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

And by leveraging distributed storage and open-source technologies, they offer a cost-effective solution for handling large data volumes. In other words, the data is stored in its raw, unprocessed form, and the structure is imposed when a user or an application queries the data for analysis or processing.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

And by leveraging distributed storage and open-source technologies, they offer a cost-effective solution for handling large data volumes. In other words, the data is stored in its raw, unprocessed form, and the structure is imposed when a user or an application queries the data for analysis or processing.

Data Management 52

Data Management Management Data Lake Data Governance 52

Knowledge Hut

NOVEMBER 3, 2023

Skills Required for MongoDB for Data Science To excel in MongoDB for data science, you need a combination of technical and analytical skills: Database Querying: It is necessary to know how to write sophisticated queries using the query language of MongoDB. Quickly pull (fetch), filter, and reduce data.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Knowledge Hut

OCTOBER 4, 2023

It is because they help you generate dynamic reports and feature data modeling, complete data comparison, and more. Understanding Power BI Requirements As I have mentioned before, Power BI is a revolutionary, remarkable program that enables high-speed data integration and the creation of plenty of reports.

BI 52

BI Systems Raw Data Data Preparation 52

Databand.ai

JULY 19, 2023

The extracted data is often raw and unstructured and may come in various formats such as text, images, audio, or video. The extraction process requires careful planning to ensure data integrity. It’s crucial to understand the source systems and their structure, as well as the type and quality of data they produce.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas. Good Hold on MongoDB and data modeling. Experience with ETL tools and data integration techniques.

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

Knowledge Hut

MARCH 19, 2024

This workflow imbalance allows unencumbered engineering while protecting data integrity. Mirror production data schemas: While masking sensitive information, reflecting production data shapes, interfaces and dependencies reduces surprises when changes reach customers. This improves security and accountability.

Pipeline-centric 52

Pipeline-centric Database-centric Software Engineering Software Engineer 52

dbt Developer Hub

FEBRUARY 7, 2022

To do so, we’ll focus on three steps: Performing the email domain extraction from the email Flagging personal emails Creating a column for corporate emails After we complete these steps, we’ll also cover a "human in the loop" step to ensure data integrity at the modelling stage.

Data Warehouse 52

Data Warehouse Data Datasets SQL 52

Striim

NOVEMBER 8, 2023

This allows for two-way integration so that information can flow from one system to another in real-time. Striim is a cloud-native Data Mesh platform that offers features such as automated data mapping, real-time data integration, streaming analytics, and more.

Architecture 52

Architecture Generalist Government Datasets 52

Monte Carlo

APRIL 20, 2023

Squatch VP of Data, IT & Security, Nick Johnson. Data integration and modeling In previous eras, data models like Data Vault were used to manually create full visibility into data lineage. A few tips for a safe migration using data lineage: Document current data schema and lineage.

Data Warehouse 52

Data Warehouse BI Data Government 52

DataKitchen

JUNE 19, 2023

Checking data at rest involves looking at syntactic attributes such as freshness, distribution, volume, schema, and lineage. Start checking data at rest with a strong data profile. The image above shows an example ‘’data at rest’ test result.

Data 40

Data Data Validation Utilities High Quality Data 40

Monte Carlo

FEBRUARY 6, 2023

Facilitating self-service data? Integrating new tooling? But just to be safe, here are a few tips: Document your current data schema and lineage. This will be important when you have to cross-reference your old data ecosystem with your new one. Better governance? What does success look like?

Data Warehouse 52

Data Warehouse AWS Data Data Validation 52

ProjectPro

JANUARY 31, 2023

It also discusses several kinds of data. Schemas are available in various shapes and sizes, and the star schema and the snowflake schema are two of the most common. Entities in a star schema are depicted as stars, whereas those in a snowflake schema are depicted as snowflakes.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

phData: Data Engineering

SEPTEMBER 27, 2024

Interoperability: By providing a standardized way of describing your customer domain, ontologies can facilitate data integration across different systems and even different brands within a larger corporation. we can evolve our customer data models faster than customers can change their minds – and that’s saying something!

Data 52

Data Raw Data Data Lake Architecture 52