Engineering at Meta

FEBRUARY 7, 2023

This new data schema was born partly out of our cartographic tiling logic, and it includes everything necessary to make a map of the world. Daylight ensures that our maps are up-to-date and free of geometry errors, vandalism, and profanity.

Entertainment 143

Entertainment Transportation Data Schemas AWS 143

ProjectPro

JUNE 6, 2025

The transformation of unstructured data into a structured format is a methodical process that involves a thorough analysis of the data to understand its formats, patterns, and potential challenges. Showcase your expertise in data modeling, emphasizing your proficiency in designing scalable and efficient data schemas.

Data Engineer 40

Data Engineer Data Engineering Engineering NoSQL 40

Towards Data Science

MAY 11, 2023

Lookup time for set and dict is more efficient than that for list and tuple , given that sets and dictionaries use hash function to determine any particular piece of data is right away, without a search. The existence of data schema at a class level makes it easy to discover the expected data shape.

Programming 98

Programming Python Data Schemas Java 98

LinkedIn Engineering

DECEMBER 19, 2023

We discuss the difference between “data” and “insights,” when you want to use qualitative (objective) data vs. qualitative (subjective) data , how to drive decisions (and provide the right data for your audience), and what data you should collect (including some thoughts about data schemas for engineering data).

Data Schemas 121

Data Schemas Software Engineer Software Engineering Data Pipeline 121

ProjectPro

JUNE 6, 2025

You can leverage access control lists (ACLs) to set permissions for workspace objects (folders, notebooks, experiments, and models), clusters, pools, tasks, data schemas, tables, views, etc., Does Delta Lake offer access controls for security and governance? using Delta Lake on Databricks.

Scala 40

Scala SQL Python Data Lake 40

Netflix Tech

DECEMBER 14, 2018

A schemaless system appears less imposing for application developers that are producing the data, as it (a) spares them from the burden of planning and future-proofing the structure of their data and, (b) enables them to evolve data formats with ease and to their liking. This is depicted in Figure 1.

Media 101

Media Database Metadata Data Schemas 101

Christophe Blefari

NOVEMBER 11, 2022

Modeling is often lead by the dimensional modeling but you can also do 3NF or data vault. When it comes to storage it's mainly a row-based vs. a column-based discussion, which in the end will impact how the engine will process data.

BI 130

BI Data Warehouse Data Database 130

Snowflake

OCTOBER 22, 2024

Rather than scrubbing or redacting sensitive fields — or worse, creating rules to generate “realistic” data from the ground up —you simply point our app at your production schema, train one of the included models, and generate as much synthetic data as you like. It’s basically an “easy button” for synthetic data.

Data Schemas 98

Data Schemas Data Machine Learning Datasets 98

Data Engineering Podcast

APRIL 7, 2019

How does the concept of a data slice play into the overall architecture of your platform? How do you manage transformations of data schemas and formats as they traverse different slices in your platform? How does the concept of a data slice play into the overall architecture of your platform?

Data Pipeline 100

Data Pipeline Pipeline-centric Database-centric AWS 100

Cloudera

JANUARY 20, 2021

The training data-set represents sensor data of an office room and with this data, a model is built to predict if the room is occupied by a person or not. In the next few sections, we’ll talk about the training data schema, classification model, batch score table, and web application.

Machine Learning 115

Machine Learning Database Data Science Building 115

Snowflake

MAY 23, 2024

Processing complex, schema-less, semistructured, hierarchical data can be extremely time-consuming, costly and error-prone, particularly if the data source has polymorphic attributes. For many data sources, the schema of the data source can change without warning.

Data Pipeline 105

Data Pipeline Raw Data Data Schemas Technology 105

Towards Data Science

NOVEMBER 6, 2024

. // All of them should already be set for existing Spark applications in one // way or another, and their complete list can be found in the UI of any // running separate Spark application on the Environment tab. amazonaws.com", // and others. )

Scala 75

Scala Java AWS Coding 75

Workfall

SEPTEMBER 18, 2023

Pre-filter and pre-aggregate data at the source level to optimize the data pipeline’s efficiency. Adapt to Changing Data Schemas: Data sources aren’t static; they evolve. Account for potential changes in data schemas and structures.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

ProjectPro

FEBRUARY 8, 2023

You can produce code, discover the data schema, and modify it. Smooth Integration with other AWS tools AWS Glue is relatively simple to integrate with data sources and targets like Amazon Kinesis, Amazon Redshift, Amazon S3, and Amazon MSK. AWS Glue automates several processes as well.

AWS 98

AWS Scala Metadata Data Lake 98

Snowflake

AUGUST 28, 2024

Data integration As a Snowflake Native App, AI Decisioning leverages the existing data within an organization’s AI Data Cloud, including customer behaviors and product and offer details. During a one-time setup, your data owner maps your existing data schemas within the UI, which fuels AI Decisioning’s models.

Cloud 79

Cloud Insurance Data Schemas Data 79

Cloudera

JULY 26, 2021

Auditabily: Data security and compliance constituents need to understand how data changes, where it originates from and how data consumers interact with it.

Generalist 119

Generalist Telecommunication Healthcare Data 119

ProjectPro

JUNE 6, 2025

Confluent enhances Kafka's capabilities with tools such as the Confluent Control Center for monitoring clusters, the Confluent Schema Registry for managing data schemas, and Confluent KSQL for stream processing using SQL -like queries.

Kafka 40

Kafka Certification AWS Retail 40

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 104

Data Warehouse Datasets Data Data Schemas 104

Confluent

SEPTEMBER 27, 2019

Today, nearly everyone uses standard data formats like Avro, JSON, and Protobuf to define how they will communicate information between services within an organization, either synchronously through RPC calls or asynchronously through Apache Kafka ® messages.

Kafka 16

Kafka Data Governance Bytes Government 16

Confluent

JUNE 13, 2019

The data from these detections are then serialized into Avro binary format. The Avro alert data schemas for ZTF are defined in JSON documents and are published to GitHub for scientists to use when deserializing data upon receipt.

Kafka 102

Kafka Bytes Data Pipeline Transportation 102

ProjectPro

JUNE 6, 2025

Source: LinkedIn Pydantic AI vs Crew AI Pydantic AI focuses on robust data validation and parsing for Python applications. Built on Pydantic, it simplifies handling complex data schemas with automatic type validation and error handling.

Building 40

Building Pipeline-centric Database-centric Data Validation 40

Towards Data Science

MARCH 9, 2023

Now that the cluster is created and the data is in order we can start the notebook by creating it on the same top-left menu used for the cluster and table setup. link] Time to meet the MLLib.

Machine Learning 98

Machine Learning Building Datasets Scala 98

ProjectPro

JUNE 6, 2025

This setup ensures efficient handling of structured data in APIs and machine learning workflows. Project Idea: Integrate Pydantic models within Langflow to define and validate data schemas. Set up agents that process and serialize data for downstream tasks.

Coding 40

Coding Building Project Database-centric 40

Grouparoo

JANUARY 30, 2022

release is our first major iteration on the user interface for creating your data pipeline. release, we added Models, which allowed data engineers to sync multiple data schemas to Destinations.

PostgreSQL 52

PostgreSQL MySQL Data Schemas SQL 52

ProjectPro

JUNE 6, 2025

Explain the difference between a Data Lake and a Data Warehouse.

Data Engineer 40

Data Engineer Data Engineering Engineering NoSQL 40

Cloudyard

OCTOBER 23, 2023

The data’s structure frequently changes, with new columns or alterations introduced. Meeting this challenge requires the development of robust data pipelines capable of modifying table columns to align with the evolving source data schema.

Data Schemas 52

Data Schemas Data Pipeline Structured Data Architecture 52

Snowflake

AUGUST 25, 2023

Snowflake is leveraging our SQL expertise to provide the best text-to-SQL capabilities that combine syntactically correct SQL with a deep understanding of customers’ sophisticated data schemas, governed and protected by their existing rights, roles and access controls.

Coding 98

Coding SQL Data Cleanse Database 98

Monte Carlo

MAY 31, 2024

A data observability tool Monte Carlo , for example, uses AI to continuously monitor data pipelines, automatically detecting anomalies and inconsistencies. By analyzing patterns and trends in the data, AI can identify issues such as missing or duplicate data, schema changes, and unexpected data values.

Data Science 52

Data Science Data Schemas Machine Learning Data Pipeline 52

Grouparoo

OCTOBER 23, 2021

release of Grouparoo is a huge step forward for data engineers using Grouparoo to reliably sync a variety of types of data to operational tools. Models enable Grouparoo to work with multiple data schemas at once. Here are the key features of the release.

AWS 52

AWS Data Schemas Datasets Data Engineer 52

Monte Carlo

MARCH 28, 2024

“There were a couple of challenges because it’s easy to break this type of pipeline and an analyst would work for quite a while to find the data he’s looking for.” It involves a contract with the client sending the data, schema registry, and pipeline owners responsible for fixing any issues.

Engineering 75

Engineering Pipeline-centric BI Google Cloud 75

Confluent

MAY 28, 2019

Therefore, not restricting access to the Schema Registry might allow an unauthorized user to mess with the service in such a way that client applications can no longer be served schemas to deserialize their data. Allow end user REST API calls to Schema Registry over HTTPS instead of the default HTTP.

Management 85

Management Kafka Java Certification 85

Towards Data Science

FEBRUARY 15, 2023

Delta Lake also refuses writes with wrongly formatted data (schema enforcement) and allows for schema evolution. Delta Lake also works with the concept of ACID transactions, that is, no partial writing caused by job failures or inconsistent readings.

Data Lake 98

Data Lake Data Warehouse Data Architecture Architecture 98

Pinterest Engineering

MAY 2, 2023

Traditionally, product engineers need to be exposed to the infra complexity, including data schema, resource provisions, and storage allocations, which involves multiple teams.

Lambda Architecture 96

Lambda Architecture Software Engineer Software Engineering Datasets 96

ProjectPro

JUNE 6, 2025

A thorough examination of the data lineage was conducted using DBT’s built-in documentation features to resolve the issue. It became clear that a recent change in the upstream data schema was not reflected in the dependent model when the relationship between the affected model and its upstream sources was analyzed.

Raw Data 40

Raw Data SQL Datasets Data Pipeline 40

DataKitchen

MAY 10, 2024

Have all the source files/data arrived on time? Is the source data of expected quality? Are there issues with data being late, truncated, or repeatedly the same? Have there been any unnoted changes to the data schema or format? I Did Not Get All The Data; I Only Got Part.

Data Ingestion 52

Data Ingestion Transportation Data Schemas High Quality Data 52

Rockset

MARCH 31, 2022

DAY 2 On day 2, as I was learning a data schema I had never seen before, I was able to write the SQL, with some amazing help from Rockset. I extracted a string value containing deeply nested JSON data with multiple arrays, subdocuments, sub arrays, etc.,

MongoDB 52

MongoDB Data Architect Data Schemas SQL 52

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 Engineer 98

Data Engineer Data Engineering Engineering BI 98