Cloudyard

JANUARY 21, 2025

In this blog, we’ll explore the significance of schema evolution using real-world examples with CSV, Parquet, and JSON data formats. Schema evolution allows for the automatic adjustment of the schema in the data warehouse as new data is ingested, ensuring data integrity and avoiding pipeline failures.

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Striim

SEPTEMBER 11, 2024

The approach to this processing depends on the data pipeline architecture, specifically whether it employs ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes. This method is advantageous when dealing with structured data that requires pre-processing before storage. In what format will the final data be stored?

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Workfall

SEPTEMBER 18, 2023

Meet Airbyte, the data magician that turns integration complexities into child’s play. In this digital era, businesses thrive on data, and making this data dance harmoniously with your analytics tools is crucial. Pre-filter and pre-aggregate data at the source level to optimize the data pipeline’s efficiency.

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Data Pipeline Raw Data Data Schemas Healthcare 93

Towards Data Science

NOVEMBER 4, 2023

Often it is a data warehouse solution (DWH) in the central part of our infrastructure. Data warehouse exmaple. 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. It will be a great tool for those with minimal Python knowledge.

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Data Engineering Data Engineer Engineering BI 98

Towards Data Science

FEBRUARY 15, 2023

Before going into further details on Delta Lake, we need to remember the concept of Data Lake, so let’s travel through some history. The main player in the context of the first data lakes was Hadoop, a distributed file system, with MapReduce, a processing paradigm built over the idea of minimal data movement and high parallelism.

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ProjectPro

FEBRUARY 8, 2023

It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., Glue uses ETL jobs for extracting data from various AWS cloud services and integrating it into data warehouses and lakes.

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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.

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Media Database Metadata Data Schemas 97

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

Data lakes, data warehouses, data hubs, data lakehouses, and data operating systems are data management and storage solutions designed to meet different needs in data analytics, integration, and processing. However, data warehouses can experience limitations and scalability challenges.

Data Management 52

Data Management Management Data Lake Data Governance 52

Cloudera

DECEMBER 21, 2023

Using the SQL AI Assistant, we can dramatically improve our work by having an intelligent SQL expert by our side, one that also knows our data schema very well. We can save time finding the right data, building the right syntax, and getting any new query started, with the generate feature.

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SQL Data Warehouse Business Analyst Data Schemas 67

Snowflake

FEBRUARY 27, 2023

Data, a driving force for business performance In light of such massive growth, data management has steadily become more complex, to the point of introducing tangible risks. ” The migration from the existing data warehouse to the Snowflake platform took six months, with both being run in parallel during the last month.

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Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. The data is loaded as-is, without any transformation.

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Monte Carlo

MARCH 28, 2024

Vimeo employs more than 35 data engineers across data platform, video analytics, enterprise analytics, BI, and DataOps teams. In 2021, Vimeo moved from a process involving big complicated ETL pipelines and data warehouse transformations to one focused on data consumer defined schemas and managed self-service analytics.

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Engineering Pipeline-centric BI Google Cloud 75

Monte Carlo

JUNE 28, 2022

Since then, Databricks has aggressively moved toward allowing users to add more structure to their data. Features like the Delta Lake and Unity Catalog , help combine the best of both the data lake and data warehouse worlds (see: data lakehouse ).

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Monte Carlo

OCTOBER 11, 2022

Exploratory data analysis Because your company is dashboard crazy and it’s easier than ever for the data engineering team to pipe in data to accommodate ad-hoc requests, discovery was challenging. The data warehouse is a mess and devoid of semantic meaning. Most can be better at clearing out legacy datasets.

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Monte Carlo

MAY 31, 2022

They operate one of the most sophisticated and robust data platforms in media. “We We have a couple of data warehouses with about a petabyte in Snowflake, 1.5 petabytes in BigQuery, and about half a petabyte in Apache HBase,” said Lior Solomon, former VP of Engineering, Data, at Vimeo.

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Rockset

MARCH 31, 2022

Our plan — the same plan I would have used if I had not known about Rockset — was to build an ETL package, extract the data from the document database, then transform it into a format that would be stored in a data warehouse. From there, the data could be ingested by any standard reporting tool.

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MongoDB Data Architect SQL Data Schemas 52

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. To explore life at Pinterest, visit our Careers page.

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Lambda Architecture Datasets Software Engineer Software Engineering 90

Monte Carlo

JUNE 9, 2022

Adopting a cloud data warehouse like Snowflake is an important investment for any organization that wants to get the most value out of their data. Most data teams, especially those early in their Snowflake journey, have yet to fully unlock full potential and value from this key investment. as well as reliability.

IT 52

IT Healthcare Raw Data Data Warehouse 52

Monte Carlo

APRIL 20, 2023

Solutions with automated data lineage capabilities constantly update these graphs and illustrate them as nodes and edges, or in other words, the objects through which the data travels and the relationship between them. This is one of the most frequent data lineage use cases leveraged by Vox. Data lineage can help!

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Data Warehouse BI Data Government 52

Rockset

DECEMBER 9, 2021

Disclaimer: Rockset is a real-time analytics database and one of the pieces in the modern real-time data stack So What is Real-Time Data (And Why Can’t the Modern Data Stack Handle It)? Every layer in the modern data stack was built for a batch-based world. The problem? Out-of-order event streams.

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Transportation BI SQL Database 52

Monte Carlo

JUNE 26, 2023

Over the last several years, Databricks has given users the ability to add more structure to the data inside their data lake. Monte Carlo can automatically monitor and alert for data schema, volume, freshness, and distribution anomalies within the data lake environment.

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dbt Developer Hub

FEBRUARY 7, 2022

This aggregation process requires an analytics warehouse, as all of these things need to be synced together outside of the application database itself to incorporate other data sources (billing / events information, past touchpoints in the CRM, etc).

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Rockset

OCTOBER 6, 2022

Consequently, we needed a data backend with the following characteristics: Scale With ~50 commits per working day (and thus at least 50 pull request updates per day) and each commit running over one million tests, you can imagine the storage/computation required to upload and process all our data.

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Cloud Academy

JANUARY 27, 2022

For example, you can learn about how JSONs are integral to non-relational databases – especially data schemas, and how to write queries using JSON. You’ll learn how to load, query, and process your data. Have experience with the JSON format It’s good to have a working knowledge of JSON.

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Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

APRIL 24, 2023

During data ingestion, raw data is extracted from sources and ferried to either a staging server for transformation or directly into the storage level of your data stack—usually in the form of a data warehouse or data lake. There are two primary types of raw data.

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Data Pipeline Building Data Ingestion BI 52

ProjectPro

JANUARY 31, 2023

Step 5: Data Validation This is the last step involved in the process of data preparation. In this step, automated procedures are used for the data to verify its accuracy, consistency, and completeness. The prepared data is then stored in a data warehouse or a similar repository.

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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.

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Monte Carlo

OCTOBER 17, 2022

Platform and data warehouse migrations aren’t something you do everyday or even every few years, but they’re becoming much more frequent as organizations seek to modernize their data infrastructure with the new capabilities being offered by Snowflake, Databricks, Google, AWS, and others. Editor’s note: We agree.

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Data Warehouse AWS MySQL Data 59

phData: Data Engineering

SEPTEMBER 27, 2024

Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation. You might choose a cloud data warehouse like the Snowflake AI Data Cloud or BigQuery. It’s like turning your data warehouse into a data distribution center.

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Data Raw Data Data Lake Architecture 52

Monte Carlo

FEBRUARY 26, 2024

Metaphor takes a modern approach to metadata by creating a social environment for data consumption, from the use of social hashtags in the data, social posts to share information, to automating a live wiki to access documentation.

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Metadata Government Data Data Governance 40

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. Hcatalog can be used to share data structures with external systems.

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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.

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Food Data SQL Hadoop 52

Ascend.io

FEBRUARY 25, 2025

Perhaps the dev environment is a small warehouse with different settings, or uses stubbed external sources that behave differently than real ones. For data teams, environment parity means your transformations, libraries, and even data schemas should mirror production as closely as possible in test environments.

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