Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

Metadata 52

Metadata Government Data Data Governance 52

Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. Introduction to the Data Mesh Architecture and its Required Capabilities. Components of a Data Mesh.

Architecture 124

Architecture Metadata Government Kafka 124

Cloudera

APRIL 1, 2024

We’re excited to share that Gartner has recognized Cloudera as a Visionary among all vendors evaluated in the 2023 Gartner® Magic Quadrant for Cloud Database Management Systems. Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics.

Cloud 104

Cloud Unstructured Data Metadata Datasets 104

AltexSoft

JULY 27, 2021

You’ll learn about the types of recommender systems, their differences, strengths, weaknesses, and real-life examples. Personalization and recommender systems in a nutshell. At the same time, the continuous growth of available data has led to information overload — when there are too many choices, complicating decision-making.

Machine Learning 76

Machine Learning Systems Algorithm Deep Learning 76

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

IT 110

IT Unstructured Data Data Architecture Government 110

AltexSoft

AUGUST 22, 2022

The landscape of enterprise data is fragmented. Organizations have data stored in public and private clouds, as well as in various on-premises data repositories. Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

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

Monte Carlo

NOVEMBER 20, 2023

Data quality is important—no doubt about it. Bad data destroys trust in both the data products and the teams that produce them. But as data quality finally takes its rightful seat at the platform table, where should new data teams start? Data Observability? Table of Contents What is data quality?

Government 69

Government Data Data Governance Datasets 69

ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

AWS 52

AWS Data Management ETL Tools Management 52

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Which Big Data tasks does Spark solve most effectively? How does it work? cost-effectiveness.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Data Engineering Weekly

MARCH 24, 2023

Why should we care about Data Creation Process? All Successful Data-Driven organizations have one thing in common; They have a high-quality & efficient data creation process. Data creation is often the differentiator between the success & the failure of a data team. The riders request a new ride.

Engineering 67

Engineering Data Transportation Database 67

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. 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?

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Engineering at Meta

FEBRUARY 6, 2024

DotSlash handles transparently fetching, decompressing, and verifying the appropriate remote artifact for the current operating system and CPU. Our continuous integration (CI) system supports special configuration for DotSlash jobs where a user must specify: A set of builds to run (these can span multiple platforms).

Metadata 117

Metadata Coding Building Project 117

AltexSoft

MAY 12, 2023

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. How much data was generated in a minute in 2013 and 2022.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Monte Carlo

JULY 13, 2023

Data-driven organizations generate, collect, and store vast amounts of data. To effectively manage and analyze this data, data engineering teams must navigate a wide range of challenges, including data access, security, compliance, and data observability. Automating self-service access.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.

Engineering 84

Engineering Raw Data Data Science Scala 84

Data Engineering Podcast

FEBRUARY 3, 2019

Summary Archaeologists collect and create a variety of data as part of their research and exploration. Open Context is a platform for cleaning, curating, and sharing this data. I got frustrated at the lack of comparative data, and I got frustrated at all the work I put into creating data that nobody would likely use.

Digital Media 100

Digital Media Media PostgreSQL Datasets 100

ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! “Data analytics is the future, and the future is NOW!

Big Data 52

Big Data Hadoop AWS Relational Database 52

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 71

Big Data Data Data Storage SQL 71

Towards Data Science

DECEMBER 23, 2023

Photo by Ian Taylor on Unsplash This tutorial guides you through an analytics use case, analyzing semi-structured data with Spark SQL. We’ll start with the data engineering process, pulling data from an API and finally loading the transformed data into a data lake (represented by MinIO ).

SQL 73

SQL Data Analytics Hadoop Raw Data 73

AltexSoft

JUNE 26, 2023

All successful companies do it: constantly collect data. While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Hepta Analytics

FEBRUARY 14, 2022

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. It is called Idempotency.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g., Mailchimp).

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake? Is Hadoop a data lake or data warehouse?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. What is Apache Spark?

Big Data 52

Big Data Data Process Process Hadoop 52

Netflix Tech

MARCH 5, 2019

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure. Canaries ), detection and improved KPIs.

Cloud 73

Cloud Building Amazon Web Services Metadata 73

Knowledge Hut

DECEMBER 21, 2023

In the present-day world, almost all industries are generating humongous amounts of data, which are highly crucial for the future decisions that an organization has to make. This massive amount of data is referred to as “big data,” which comprises large amounts of data, including structured and unstructured data that has to be processed.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Zalando Engineering

JANUARY 30, 2023

Our Incident Playbooks cover emergency procedures to initiate in case a certain set of conditions is met, for example when one of our systems is overloaded and the existing resiliency measures (e.g. When the bigger system context is considered, there are more options available to mitigate issues. processing of price updates).

Management 57

Management Metadata Software Engineer Software Engineering 57

Ascend.io

JULY 19, 2023

Ready or not, data mesh is fast becoming an indispensable part of the data landscape. As data leaders, the question isn’t if you’ll cross paths with this emerging architectural pattern. Consider this your primer to stop overthinking, start acting, and truly harness the power of data mesh.

Data Governance 40

Data Governance Government Metadata Data 40

Monte Carlo

JANUARY 25, 2023

Over the past year, data contracts have taken the data world by storm as a novel approach to ensuring data quality at scale in production services. In the last couple of posts , I’ve focused on implementing data contracts in production services. Where to start implementing data contracts?

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

Silectis

JANUARY 3, 2021

If you’re new to data engineering or are a practitioner of a related field, such as data science, or business intelligence, we thought it might be helpful to have a handy list of commonly used terms available for you to get up to speed. Big Data Large volumes of structured or unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Cloudera

SEPTEMBER 17, 2020

Today’s data tool challenges. By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. .

Unstructured Data 81

Unstructured Data Data Warehouse Pharmaceutical MySQL 81

AltexSoft

JULY 29, 2022

Depending on how you measure it, the answer will be 11 million newspaper pages or… just one Hadoop cluster and one tech specialist who can move 4 terabytes of textual data to a new location in 24 hours. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Data Engineering Podcast

JULY 1, 2018

Summary Data is often messy or incomplete, requiring human intervention to make sense of it before being usable as input to machine learning projects. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform.

Metadata 100

Metadata Machine Learning Data Preparation Data Collection 100

Ascend.io

JUNE 8, 2023

Because of its transformative potential, data has graduated from being merely a by-product of business operations to a critical asset in its own right. Is it possible to treat data not just as a necessary operational output, but as a product that holds immense strategic value? Without them, data products can’t exist.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Precisely

OCTOBER 5, 2023

How much data is your business generating each day? While answers will vary by organization, chances are there’s one commonality: it’s more data than ever before. But what do you do with all that data? How do you turn that raw data into actionable insights? That’s where data enrichment comes in.

Raw Data 67

Raw Data Insurance Datasets Telecommunication 67

Netflix Tech

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy.

Data Warehouse 97

Data Warehouse Datasets Data Big Data 97