Data Science Blog: Data Engineering

MAY 20, 2024

They transform data into a consistent format for users to consume. Automated data pipelines eliminate human errors when manipulating data. Data professionals save time spent on data processing transformation. It offers full BI-Stack Automation, from source to data warehouse through to frontend.

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Snowflake

JULY 18, 2023

Data infrastructure that makes light work of complex tasks Built as a connected application from day one, the anecdotes Compliance OS uses the Snowflake Data Cloud for data ingestion and modeling, including a single cybersecurity data lake where all data can be analyzed within Snowflake.

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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. This feature allows for a more flexible exploration of data.

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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. This feature allows for a more flexible exploration of data.

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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. This feature allows for a more flexible exploration of data.

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Towards Data Science

DECEMBER 4, 2023

A robust data infrastructure is a must-have to compete in the F1 business. We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. in alphabetical order: Apache Airflow, Azure Data Factory, DBT, Google DataForm, …).

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RandomTrees

FEBRUARY 6, 2024

Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.

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The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

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The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

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The Modern Data Company

MAY 10, 2023

In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions , we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Consider whether you need a solution that supports one or multiple data formats.

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Knowledge Hut

MARCH 28, 2024

Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB. GDPR, HIPAA), and industry standards.

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Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage).

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AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

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Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

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Monte Carlo

JANUARY 12, 2024

On the data lake side, Databricks has launched Unity Catalog to help bring more metadata, structure, and governance to data assets. Real-time data/insights Being able to access real-time data for analysis might sound like overkill to some, but that’s just no longer the case.

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AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

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AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

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Monte Carlo

AUGUST 19, 2023

To make things a little easier, I’ve outlined the six must-have layers you need to include in your data platform and the order in which many of the best teams choose to implement them. The five must-have layers of a modern data platform Second to “how do I build my data platform?”,

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Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions.

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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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AltexSoft

MAY 12, 2023

Unstructured data , on the other hand, is unpredictable and has no fixed schema, making it more challenging to analyze. Without a fixed schema, the data can vary in structure and organization. Once the unstructured data has been collected, the next step is to store and process this data effectively.

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AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

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Snowflake

JUNE 27, 2023

Supporting mission criticality with enhanced native data governance, new Snowflake UIs, a growing compliance footprint, and updated cross-cloud business continuity At Snowflake, we’re committed to providing best-in-class native data governance features for customers entrusting our platform with their data.

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Monte Carlo

FEBRUARY 21, 2023

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake. That’s a model worth looking at when it comes to data governance,” says Bob.

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Monte Carlo

FEBRUARY 21, 2023

These include: Seamless integration A data fabric’s ability to integrate seamlessly with various APIs and software-delivery kits (SDKs) truly differentiates this framework from another type of data management: the ubiquitous data lake. That’s a model worth looking at when it comes to data governance,” says Bob.

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Knowledge Hut

APRIL 25, 2024

The ability to pull data in real time from many sources. They simplify data processing for our brains and give readers a quick overview of past, present, and future performance by helping the user to visualize otherwise complex and weighty raw data. Data from diverse sources must be mixed. It is also limiting.

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Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

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Monte Carlo

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

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Monte Carlo

APRIL 4, 2023

Data mesh is a type of platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Unlike data warehouses and data lakes, the data mesh decentralizes data ownership.

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Striim

AUGUST 31, 2023

Kappa Architecture combines streaming and batch while simultaneously turning data warehouses and data lakes into near real-time sources of truth. Overview of kappa architecture Kappa architecture is a powerful data processing architecture that enables near-real-time data processing.

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Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

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DataKitchen

JUNE 12, 2023

We had been talking about “Agile Analytic Operations,” “DevOps for Data Teams,” and “Lean Manufacturing For Data,” but the concept was hard to get across and communicate. I spent much time de-categorizing DataOps: we are not discussing ETL, Data Lake, or Data Science.

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Databand.ai

JULY 5, 2023

Data catalog and lineage tools: These tools provide visibility into data lineage by tracking the origin, transformation, and consumption of data across the data pipeline. They help organizations understand the dependencies between data sources, processes, and systems, enabling better data governance and impact analysis.

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phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

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Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

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Cloudera

NOVEMBER 18, 2020

The Advanced Analytics team supporting the businesses of Merck KGaA, Darmstadt, Germany was able to establish a data governance framework within its enterprise data lake. This enabled Merck KGaA to control and maintain secure data access, and greatly increase business agility for multiple users.

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The Modern Data Company

APRIL 15, 2024

The Role of a Data Model Explained Think of a data model as the ultimate organizer in the vast library of your company’s data. Its job, from its position near the end of the data processing line, is similar to that of a librarian who: Answers queries from various departments looking for specific insights.

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