ProjectPro

JUNE 6, 2025

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

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

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

ProjectPro

JUNE 6, 2025

For example, the granularity for time-series data might be based on intervals of hours, days, months, or years. Data Warehouse Capgemini Interview Questions) The fact table is a central table in data schemas. It is usually found in the center of a star or snowflake schema, surrounded by a dimension table.

Data Warehouse 40

Data Warehouse Data Mining Recruitment Database 40

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 Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

Engineering at Meta

DECEMBER 19, 2024

Therefore: Glean doesnt decide for you what data you can store. Indeed, most languages that Glean indexes have their own data schema and Glean can store arbitrary non-programming-language data too. The data is ultimately stored using RocksDB , providing good scalability and efficient retrieval.

Coding 85

Coding Programming Language SQL Programming 85

Picnic Engineering

JULY 15, 2025

Data structure: Data arrives in different raw formats, e.g. JSON, XML, CSV. Supplier’s data schema is out of our control. Data integrity: Sensitive commercial information must be encrypted. Certain product information requires prior context. Older updates might arrive after newer ones. Detect changes early.

Data Ingestion 52

Data Ingestion Programming Language Data Integration Data Warehouse 52

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

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 76

Scala Java AWS Hadoop 76

Cloudyard

JANUARY 21, 2025

Conclusion Schema evolution is a vital feature that allows data pipelines to remain flexible and resilient as data structures change over time. Whether dealing with CSV, Parquet, or JSON data, schema evolution ensures that your data processing workflows continue to function smoothly, even when new columns are added or removed.

Data Schemas 59

Data Schemas Data Pipeline Data Warehouse Data Storage 59

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

ProjectPro

JUNE 6, 2025

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 40

Big Data Hadoop Relational Database AWS 40

ProjectPro

JUNE 6, 2025

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. Follows SQL Dialect and is a declarative language.

Hadoop 40

Hadoop Metadata SQL Database 40

ProjectPro

JUNE 6, 2025

Additionally, you might wish to test the data schema to ensure that it hasn't changed and won't unintentionally provide erroneous input features. Understanding the data and its domain is necessary for unit testing so that you can prepare the precise assertions to make as part of the ML project.

AWS 45

AWS Pipeline-centric Database-centric Machine Learning 45

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 Project Building Database-centric 40

Start Data Engineering

OCTOBER 14, 2024

Using nested data types in data processing 3.3.1. STRUCT enables more straightforward data schema and data access 3.3.2. Nested data types can be sorted 3.3.3. Use STRUCT for one-to-one & hierarchical relationships 3.2. Use ARRAY[STRUCT] for one-to-many relationships 3.3.

SQL 130

SQL Data Schemas Data Coding 130

Start Data Engineering

MARCH 1, 2025

Introduction If you have worked at a company that moves fast (or claims to), you’ve inevitably had to deal with your pipelines breaking because the upstream team decided to change the data schema!

Data Schemas 130

Data Schemas Management Data Process 130

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 144

Entertainment Transportation Data Schemas AWS 144

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 Designing 121

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 103

Media Database Metadata Data Schemas 103

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

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

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 96

Data Data Schemas Machine Learning Datasets 96

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 103

Data Pipeline Raw Data Data Schemas Technology 103

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

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

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 106

Data Warehouse Datasets Data Big Data 106

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 Python 102

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 67

Cloud Insurance Data Schemas Algorithm 67

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

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

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

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 Engineering 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