Towards Data Science

DECEMBER 4, 2023

Anyways, I wasn’t paying enough attention during university classes, and today I’ll walk you through data layers using —  guess what  —  an example. Business Scenario & Data Architecture Imagine this: next year, a new team on the grid, Red Thunder Racing, will call us (yes, me and you) to set up their new data infrastructure.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Lake 98

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. As a result, monitoring data in real time was often an afterthought.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Data 98

Monte Carlo

JANUARY 23, 2023

When it comes to the data community, there’s always a debate broiling about something— and right now “data mesh vs data lake” is right at the top of that list. In this post we compare and contrast the data mesh vs data lake to illustrate the benefits of each and help discover what’s right for your data platform.

Data Lake 52

Data Lake Architecture Business Intelligence Unstructured Data 52

Monte Carlo

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Data discovery tools and platforms can help.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Government 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

DataKitchen

AUGUST 3, 2021

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. First-generation – expensive, proprietary enterprise data warehouse and business intelligence platforms maintained by a specialized team drowning in technical debt.

Pharmaceutical 98

Pharmaceutical Data Lake Data Architecture Architecture 98

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

DataKitchen

NOVEMBER 29, 2021

Data Gets Meshier. 2022 will bring further momentum behind modular enterprise architectures like data mesh. The data mesh addresses the problems characteristic of large, complex, monolithic data architectures by dividing the system into discrete domains managed by smaller, cross-functional teams.

Data Lake 98

Data Lake Manufacturing Architecture Data Architecture 98

AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Data Engineering Podcast

NOVEMBER 18, 2019

Summary With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo!

Data Lake 100

Data Lake Scala Data Warehouse Hadoop 100

Edureka

APRIL 9, 2025

Imagine being in charge of creating an intelligent data universe where collaboration, analytics, and artificial intelligence all work together harmoniously. Development of Some Relevant Skills and Knowledge Data Engineering Fundamentals: Theoretical knowledge of data loading patterns, data architectures, and orchestration processes.

Engineering 40

Engineering Data Ingestion Data Lake Programming Language 40

DataKitchen

NOVEMBER 4, 2021

Data organizations often have a mix of centralized and decentralized activity. DataOps concerns itself with the complex flow of data across teams, data centers and organizational boundaries. It expands beyond tools and data architecture and views the data organization from the perspective of its processes and workflows.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Cloudera

NOVEMBER 18, 2020

The technological linchpin of its digital transformation has been its Enterprise Data Architecture & Governance platform. It hosts over 150 big data analytics sandboxes across the region with over 200 users utilizing the sandbox for data discovery.

Medical 123

Medical Banking Telecommunication Government 123

Data Engineering Podcast

SEPTEMBER 18, 2019

They have built the “missing” Amazon Redshift console – it’s an amazing analytics product for data engineers to find and re-write slow queries and gives actionable recommendations to optimize data pipelines. WeWork, Postmates, and Medium are just a few of their customers.

Hadoop 100

Hadoop Data Lake BI Kafka 100

Data Engineering Podcast

OCTOBER 21, 2019

This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC. Raghu Murthy, founder and CEO of Datacoral built data infrastructures at Yahoo! This week’s episode is also sponsored by Datacoral, an AWS-native, serverless, data infrastructure that installs in your VPC.

Cloud 100

Cloud Hadoop Data Lake Programming Language 100

AltexSoft

OCTOBER 30, 2021

They’re integral specialists in data science projects and cooperate with data scientists by backing up their algorithms with solid data pipelines. Juxtaposing data scientist vs engineer tasks. One data scientist usually needs two or three data engineers. Providing data access tools.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Cloudera

DECEMBER 1, 2022

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. RI is a global leader in the design and deployment of large-scale, production-level modern data platforms for the world’s largest enterprises.

Data Lake 84

Data Lake Data Pipeline Analytics Application Architecture 84

Snowflake

JULY 22, 2024

Determining an architecture and a scalable data model to integrate more source systems in the future. The benefits of migrating to Snowflake start with its multi-cluster shared data architecture, which enables scalability and high performance. top modernizing your data lake with Snowflake, watch our on demand webinar.

Government 117

Government Data Cloud Hadoop 117

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Central to this transformation are two shifts. Let’s take a closer look.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

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”.

Architecture 124

Architecture Metadata Kafka Government 124

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. 1: Multi-function analytics . Flexible and open file formats.

Metadata 92

Metadata Data Architecture BI Machine Learning 92

AltexSoft

FEBRUARY 11, 2023

To get a better understanding of a data architect’s role, let’s clear up what data architecture is. Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer?

Data Architect 87

Data Architect Certification Generalist Big Data 87

Data Engineering Weekly

MARCH 3, 2024

link] Zendesk: dbt at Zendesk The Zendesk team shares their journey of migrating legacy data pipelines to dbt, focusing on making them more reliable, efficient, and scalable. link] Martin Chesbrough: How to Build a Modern Data Team? This approach helps maintain accuracy, relevance, and compliance in generative AI applications.

Data Engineering 67

Data Engineering Data Engineer Engineering Pipeline-centric 67

Monte Carlo

MAY 14, 2024

As organizations seek greater value from their data, data architectures are evolving to meet the demand — and table formats are no exception. This development was crucial for enabling both batch and streaming data workflows in dynamic environments, ensuring consistency and durability in big data processing.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Monte Carlo

AUGUST 31, 2023

As the data analyst or engineer responsible for managing this data and making it usable, accessible, and trustworthy, rarely a day goes by without having to field some request from your stakeholders. But what happens when the data is wrong? In our opinion, data quality frequently gets a bad rep.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Databand.ai

AUGUST 30, 2023

It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

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.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Knowledge Hut

MARCH 28, 2024

They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines. Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Develop data models, data governance policies, and data integration strategies.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

DataKitchen

JANUARY 3, 2022

There’s a recent trend toward people creating data lake or data warehouse patterns and calling it data enablement or a data hub. DataOps expands upon this approach by focusing on the processes and workflows that create data enablement and business analytics. DataOps Process Hub.

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

Data Engineering Weekly

DECEMBER 16, 2024

Evolution of Data Lake Technologies The data lake ecosystem has matured significantly in 2024, particularly in table formats and storage technologies. Infrastructure Cost Management PayPal achieved remarkable results by leveraging Spark 3 and NVIDIA's GPUs , reducing cloud costs by up to 70% for their big data pipelines.

Data Engineering 62

Data Engineering Data Engineer Engineering Unstructured Data 62

Monte Carlo

JULY 30, 2020

In the age of self-service business intelligence , nearly every company considers themselves a data-first company, but not every company is treating their data architecture with the level of democratization and scalability it deserves. Your company, for one, views data as a driver of innovation. You will not regret it).

IT 45

IT Data Lake Architecture Data Architecture 45

Snowflake

MARCH 14, 2023

Snowflake helps security teams consolidate their risk data and perform risk analytics to combat cybersecurity threats. Time-intensive tasks such as gathering evidence for audits and reviews have been replaced by streaming data pipelines that continuously record activity and configuration events.

Cloud 52

Cloud Insurance Data Lake Banking 52

Knowledge Hut

NOVEMBER 17, 2023

As Azure Data Engineers, they'll be responsible for creating and looking after solutions that use data to help the company. They enhance data pipelines, transform data, and guarantee the accuracy, integrity, and compliance of the data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Knowledge Hut

NOVEMBER 17, 2023

To provide end users with a variety of ready-made models, Azure Data engineers collaborate with Azure AI services built on top of Azure Cognitive Services APIs. They work with various stakeholders to provide real-time data analytics, maintain data quality and integrity, and deliver insightful data to the business.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

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.

Scala 64

Scala Data Lake Machine Learning BI 64

Knowledge Hut

SEPTEMBER 26, 2023

Key connectivity features include: Data Ingestion: Databricks supports data ingestion from a variety of sources, including data lakes, databases, streaming platforms, and cloud storage. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Machine Learning Pipeline-centric 52

Silectis

JANUARY 3, 2021

Big Data Processing In order to extract value or insights out of big data, one must first process it using big data processing software or frameworks, such as Hadoop. Big Query Google’s cloud data warehouse. Data Catalog An organized inventory of data assets relying on metadata to help with data management.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40