Ascend.io

JULY 19, 2023

Establish clear data governance policies. The policies should outline rules and standards for data. These should be explicit and prescriptive, addressing the 5 aspects below: Domain and business key definitions: Clearly define your business keys and the domains they belong to. Develop a data product lifecycle framework.

Data Governance 40

Data Governance Government Metadata Data 40

Ascend.io

JUNE 8, 2023

Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape. Data stewards are thus the best contributors to the content of data catalogs.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

ProjectPro

JANUARY 24, 2023

You can leverage AWS Glue to discover, transform, and prepare your data for analytics. In addition to databases running on AWS, Glue can automatically find structured and semi-structured data kept in your data lake on Amazon S3, data warehouse on Amazon Redshift, and other storage locations.

AWS 52

AWS ETL Tools Data Lake Scala 52

AltexSoft

JUNE 26, 2023

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data. How systems exchange data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Netflix Tech

OCTOBER 31, 2018

The curious reader might have noticed that a majority of these characteristics relate to properties of the data managed by NMDB. Specifically, structured data that is modeled around the notion of a media timeline, with additional spatial properties. called “ N etflix M edia D ata B ase” (NMDB) that is used to address them.

Media 54

Media Metadata Data MongoDB 54

Data Engineering Weekly

MARCH 24, 2023

About Schemata Schemata is a schema modeling framework for decentralized domain-driven ownership of data. Schemata combine a set of standard metadata definitions for each schema & data field and a scoring algorithm to provide a feedback loop on how efficient the data modeling of your data warehouse is.

Engineering 67

Engineering Data Transportation Database 67

AltexSoft

JULY 29, 2022

How HDFS master-slave structure works. A master node called NameNode maintains metadata with critical information, controls user access to the data blocks, makes decisions on replications, and manages slaves. How data engineering works under the hood. If you divide it by 128 MB, you’ll have 8 data blocks.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 96

Media Database Metadata Data Schemas 96

Towards Data Science

FEBRUARY 15, 2023

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. show() The history object is a Spark Data Frame. delta_table.history().select("version",

Data Lake 76

Data Lake Data Warehouse Hadoop Architecture 76

Grouparoo

JANUARY 11, 2022

A data warehouse is a unified repository where data from diverse sources undergo aggregation and integration into a usable source of information. To achieve this, a data warehouse will require processes to gather and integrate data, manage data quality, create metadata, and support any regulatory compliance and governance procedures.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

JANUARY 25, 2023

That being said, it tends to be much easier to reprocess data in the data warehouse when we do find bad records, whereas that might not be possible in a streaming environment. Definition of data contracts Similar to contracts in production services, contracts in the warehouse should be implemented in code and version controlled.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

AltexSoft

MARCH 2, 2023

Data modeling involves creating a conceptual representation of data objects and their relationships to each other, as well as the rules governing those relationships. To design an effective data governance program, it’s crucial to choose an operational model that fits your business size and structure.

Data Governance 52

Data Governance Government Programming Healthcare 52

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

AltexSoft

NOVEMBER 1, 2023

NER for structuring unstructured data NER plays a pivotal role in converting unstructured text into structured data. However, its design might not be optimal for tasks where entity definitions are ambiguous or when the key information is in the middle of entities.

Deep Learning 52

Deep Learning Machine Learning Datasets Algorithm 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

ProjectPro

OCTOBER 28, 2015

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., However, it is not very suitable for queries requiring low latency or interactive queries.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

DECEMBER 10, 2021

CSPack uses the data from the service definition and service configuration files to define the content within a package. Azure Table Storage- Azure Tables is a NoSQL database for storing structured data without a schema. It lets you store organized NoSQL data in the cloud and provides a schemaless key/attribute storage.

BI 52

BI Cloud Computing SQL Database 52

ProjectPro

OCTOBER 20, 2021

It has automated components of the traditional ML Flow from data acquisition, experimentation and even logging—definitely, a must-try within the Azure ecosystem. A feature store is a modern, elegant solution to leverage data prep work from previous runs or other teams as well.

Machine Learning 52

Machine Learning Algorithm Data Science Government 52

AltexSoft

MAY 12, 2023

By understanding these aspects comprehensively, you can harness the true potential of unstructured data and transform it into a strategic asset. What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Language It is a procedural data flow language. Hive Metastore is a central repository that stores metadata in external database.

Hadoop 40

Hadoop Metadata SQL Database 40

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Architecture 123

Architecture Metadata Kafka Government 123

AltexSoft

MARCH 14, 2023

While the open-source part is definitely a good thing, it requires a high level of expertise as there’s often no GUI. That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

MARCH 30, 2023

Shell, Adobe, Burberry, Columbia, Bayer — you definitely know the names. The answer is simple: They use the same technology to make the most of data. Along with thousands of other data-driven organizations from different industries, the above-mentioned leaders opted for Databrick to guide strategic business decisions.

Scala 64

Scala Data Lake BI Google Cloud 64

phData: Data Engineering

JULY 18, 2023

Demands on the cloud data warehouse are also evolving to require it to become more of an all-in-one platform for an organization’s analytics needs. Enter Snowflake The Snowflake Data Cloud is one of the most popular and powerful CDW providers. Allowing data diff analysis and code generation.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52