Towards Data Science

FEBRUARY 15, 2023

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

Data Lake 98

Data Lake Data Warehouse Architecture Data Architecture 98

ProjectPro

JUNE 6, 2025

Define Big Data and Explain the Seven Vs of Big Data. Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. RDBMS stores structured data. RDBMS uses high-end servers.

Big Data 40

Big Data Hadoop Relational Database NoSQL 40

Towards Data Science

JANUARY 17, 2024

Parquet vs ORC vs Avro vs Delta Lake Photo by Viktor Talashuk on Unsplash The big data world is full of various storage systems, heavily influenced by different file formats. These are key in nearly all data pipelines, allowing for efficient data storage and easier querying and information extraction.

Big Data 97

Big Data Data Data Storage SQL 97

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. You can use Glue's G.1X

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AWS Scala Metadata Data Lake 98

Databand.ai

JULY 19, 2023

The ELT process relies heavily on the power and scalability of modern data storage systems. By loading the data before transforming it, ELT takes full advantage of the computational power of these systems. This approach allows for faster data processing and more flexible data management compared to traditional methods.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Knowledge Hut

NOVEMBER 3, 2023

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. Why Use MongoDB for Data Science? Quickly pull (fetch), filter, and reduce data.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Monte Carlo

JUNE 28, 2022

Monte Carlo can automatically monitor and alert for data schema, volume, freshness, and distribution anomalies within the data lake environment. Delta Lake The Delta Lake is an open source storage layer that sits on top of and imbues an existing data lake with additional features that make it more akin to a data warehouse.

Data Lake 52

Data Lake Metadata Data Warehouse AWS 52

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. The path will help you understand common data formats you might encounter as a data engineer, starting with SQL.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas. Writing efficient and scalable MongoDB queries. Integrating MongoDB with front-end and backend systems.

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

Striim

NOVEMBER 8, 2023

Data consistency is ensured through uniform definitions and governance requirements across the organization, and a comprehensive communication layer allows other teams to discover the data they need. Marketing teams should have easy access to the analytical data they need for campaigns.

Architecture 52

Architecture Generalist Government Data 52

ProjectPro

JANUARY 31, 2023

Define Big Data and Explain the Seven Vs of Big Data. Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. RDBMS stores structured data. RDBMS uses high-end servers.

Big Data 52

Big Data Hadoop Relational Database NoSQL 52

Monte Carlo

APRIL 20, 2023

This way no decisions get made on bad data and our team becomes a proactive part of the solution,” said then Senior Director of Data at Freshly, Vitaly Lilich. Data access and enablement Data lineage is essential to data quality, but that is far from its only use case. Analyze your current schema and lineage.

Data Warehouse 52

Data Warehouse BI Government Data 52

ProjectPro

JUNE 6, 2025

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

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

phData: Data Engineering

SEPTEMBER 27, 2024

It’s like building your own data Avengers team, with each component bringing its own superpowers to the table. Here’s how a composable CDP might incorporate the modeling approaches we’ve discussed: Data Storage and Processing : This is your foundation. Those days are gone!

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Data Lake Data Raw Data Data Architecture 52