Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

Metadata 52

Metadata Government Data Data Governance 52

Monte Carlo

JUNE 26, 2023

After launching our partnership with Databricks last year, Monte Carlo has aggressively expanded our native Databricks and Apache Spark™ integrations to extend data observability into the Delta Lake and Unity Catalog, and in the process, drive even more value for Databricks customers.

Data Lake 52

Data Lake Metadata Bytes Google Cloud 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 94

Media Database Metadata Data Schemas 94

ProjectPro

NOVEMBER 22, 2021

The StructType and StructField classes in PySpark are used to define the schema to the DataFrame and create complex columns such as nested struct, array, and map columns. StructType is a collection of StructField objects that determines column name, column data type, field nullability, and metadata. appName('ProjectPro').getOrCreate()

Hadoop 52

Hadoop Python Datasets Metadata 52

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.

Relational Database 52

Relational Database Banking Media Pharmaceutical 52

dbt Developer Hub

NOVEMBER 28, 2021

This could just as easily have been Snowflake or Redshift, but I chose BigQuery because one of my data sources is already there as a public dataset. dbt seeds data from offline sources and performs necessary transformations on data after it's been loaded into BigQuery. I spun up an instance using its docker/up.sh

Raw Data 52

Raw Data Metadata Datasets Database 52

Towards Data Science

FEBRUARY 15, 2023

Delta Lake also refuses writes with wrongly formatted data (schema enforcement) and allows for schema evolution. It contains a detailed description of each operation performed, including all the metadata about the operation. show() The history object is a Spark Data Frame. delta_table.history().select("version",

Data Lake 80

Data Lake Data Warehouse Hadoop Data Architecture 80

Monte Carlo

MARCH 2, 2023

Otherwise you may produce more data anomalies than you prevent. Data Contracts Image courtesy of Andrew Jones. You can think of data contracts as circuit breakers, but for data schemas instead of the data itself.

Food 52

Food Data SQL Data Pipeline 52

Netflix Tech

OCTOBER 27, 2020

As the paved path for moving data to key-value stores, Bulldozer provides a scalable and efficient no-code solution. Users only need to specify the data source and the destination cluster information in a YAML file. Bulldozer provides the functionality to auto-generate the data schema which is defined in a protobuf file.

Data Warehouse 97

Data Warehouse Datasets Data Big Data 97

Netflix Tech

OCTOBER 31, 2018

The Media Document Model The Media Document model is intended to be a flexible framework that can be used to represent static as well as dynamic (varying with time and space) metadata for various media modalities. Timing Model We use the Media Document model to represent timed metadata for our media assets.

Media 54

Media Metadata Data MongoDB 54

Ascend.io

MAY 19, 2023

The holistic approach of the post-modern data stack translates into numerous benefits: First, it accelerates pinpointing and troubleshooting pipeline hotspots with a single console that observes the entire data pipeline and all its processes. Change Enablement In the world of data, change is as inevitable as the rising sun.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Zalando Engineering

SEPTEMBER 28, 2022

System architecture context relevant for the Landing Page stack Content Data Model The actual content of a landing page is managed within Contentful as "entries"; each entry-type having its own data schema definition, validation rules and a content-upload UI for the content editors. The main entry is the landing page itself.

Certification 52

Certification Architecture Data Schemas Metadata 52

Zalando Engineering

SEPTEMBER 8, 2021

We want to avoid unwanted data coupling and allow Renderers to be reused in other contexts with minimal risks. Renderers have access to Zalando’s GraphQL Mutation APIs which allows remote data to be modified. Rendering Engine Rendering Engine is the framework powering the Renderers. The page rendering always starts with an Entity.

Engineering 52

Engineering Computer Science Data Schemas Coding 52

ProjectPro

JANUARY 31, 2023

Why is HDFS only suitable for large data sets and not the correct tool for many small files? NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. And storing these metadata in RAM will become problematic.

Big Data 52

Big Data Hadoop AWS Relational Database 52

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. Hive requires a well-defined Schema. Language It is a procedural data flow language. Hive stores the metadata in RDBMS rather than HDFS.

Hadoop 40

Hadoop Metadata SQL Database 40

Confluent

APRIL 30, 2019

For this specific case, when the StreamBuilder#build() method is called, Streams will “push up” the repartitioning phase of the logical plan based on the captured metadata before compiling it to the processor topology. With the topology optimization framework added to the Streams DSL layer in Kafka 2.1,

Kafka 90

Kafka Coding Process Bytes 90

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

Cloudera

JANUARY 11, 2021

In addition, it can be challenging to keep a strong control of costs and to know where your data resides in an effort to serve your business well. Shared Data Experience (SDX), a shared persistent layer of access models, lineage-audit trace, and all metadata, is the key to the Cloudera data lake implementation.

Data Warehouse 61

Data Warehouse Pharmaceutical Data Lake BI 61

Towards Data Science

NOVEMBER 4, 2023

What I like about it is that it makes it really easy to work with various data file formats, i.e. SQL, XML, XLS, CSV and JSON. Among other benefits, I like that it works well with semi-complex data schemas. Pandas is an absolute beast in the world of data and there is no need to cover it’s capabilities in this story. .")

Data Engineering 85

Data Engineering Data Engineer Engineering BI 85

Monte Carlo

APRIL 20, 2023

I can surface ownership metadata and alert the relevant owners to make sure the appropriate changes are made so these breakages never happen. A few tips for a safe migration using data lineage: Document current data schema and lineage. Analyze your current schema and lineage.

Data Warehouse 52

Data Warehouse BI Government Data 52