Netflix Tech

AUGUST 1, 2022

A Data Movement and Processing Platform @ Netflix By Bo Lei , Guilherme Pires , James Shao , Kasturi Chatterjee , Sujay Jain , Vlad Sydorenko Background Realtime processing technologies (A.K.A stream processing) is one of the key factors that enable Netflix to maintain its leading position in the competition of entertaining our users.

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Process Transportation Kafka Entertainment 108

AltexSoft

AUGUST 25, 2021

And this technology of Natural Language Processing is available to all businesses. Available methods for text processing and which one to choose. What is Natural Language Processing? Natural language processing or NLP is a branch of Artificial Intelligence that gives machines the ability to understand natural human speech.

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Process Deep Learning Datasets Machine Learning 139

Data Science Blog: Data Engineering

NOVEMBER 15, 2023

In addition to Business Intelligence (BI), Process Mining is no longer a new phenomenon, but almost all larger companies are conducting this data-driven process analysis in their organization. This aspect can be applied well to Process Mining, hand in hand with BI and AI. This is where the processes are filtered and analyzed.

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Architecture Database-centric Process BI 52

LinkedIn Engineering

MARCH 23, 2023

Co-Authors: Yuhong Cheng , Shangjin Zhang , Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. By unifying these pipelines, we have saved 94% of processing time. Samza , Spark and Apache Flink ).

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Process Lambda Architecture Kafka Datasets 97

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. It involves bringing together people, processes, and technology to enable data-driven decision making and improve the efficiency of data-related workflows.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

LinkedIn Engineering

DECEMBER 6, 2022

With a reasonably sizable footprint of servers in data centers, LinkedIn is responsible for ensuring that these hosts are always on an operating system (OS) version deemed the ���latest and greatest��� for all intents and purposes. Another big challenge for this ecosystem was how snapshotting was conducted.

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Systems Metadata Datasets Designing 55

Edureka

AUGUST 2, 2023

Data processing analysts can be useful in this situation. Let’s take a deep dive into the subject and look at what we’re about to study in this blog: Table of Contents What Is Data Processing Analysis? What Does a Data Processing Analyst Do? What does a Data Processing Analysts do ?

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Data Process Process Data Cleanse Data Mining 52

Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. If some terminologies in the blog around Machine Learning seems unfamiliar to you, don’t worry we have the Best Data Science courses to help you out.

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Machine Learning Process Datasets Raw Data 52

Knowledge Hut

FEBRUARY 29, 2024

Learning Data Analysis in Excel Data analysis is a process of inspecting, cleaning, transforming and modelling data with an objective of uncover the useful knowledge, results and supporting decision. Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered.

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Data Science Datasets Database Design Machine Learning 98

Databand.ai

JULY 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. However, the ultimate goal is simple: to prepare data for immediate use.

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Data Ingestion Process Data Cleanse Data Governance 52

Data Science Blog: Data Engineering

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. Scalability Tailored for Data Needs Data Warehouse Systems often require to scale quickly to handle larger datasets or more queries.

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Data Warehouse Coding Systems Cloud 52

Tweag

APRIL 19, 2023

Most training pipelines and systems are designed to handle fairly small, sub-megapixel images. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. A single hospital makes many captures a day, producing terabytes of such data to store and process.

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Medical Process Cloud Bytes 60

Engineering at Meta

MAY 22, 2024

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency. Over time, these divergent systems created a fragmented data environment with little reuse across systems, which eventually slowed our engineering innovation.

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Data Management Management Data SQL 86

Striim

NOVEMBER 17, 2023

Striim serves as a real-time data integration platform that seamlessly and continuously moves data from diverse data sources to destinations such as cloud databases, messaging systems, and data warehouses, making it a vital component in modern data architectures. In this blog, we will focus on a PostgreSQL database.

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Machine Learning Data Process PostgreSQL Process 52

phData: Data Engineering

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Efficiency through being able to streamline data storage and retrieval processes.

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Data Lake Process Metadata Data Warehouse 52

Databand.ai

JULY 6, 2023

Data Integrity Testing: Goals, Process, and Best Practices Niv Sluzki July 6, 2023 What Is Data Integrity Testing? Data integrity testing refers to the process of validating the accuracy, consistency, and reliability of data stored in databases, data warehouses, or other data storage systems.

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Data Integration Process Data Validation Data Governance 52

DataKitchen

MAY 10, 2024

This blog post introduces five critical use cases for data observability, each pivotal in maintaining the integrity and usability of data throughout its journey in any enterprise. Data ingestion observability helps organizations avoid costly mistakes when poor-quality data is used in critical business processes.

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Data Ingestion Datasets Data Coding 56

ProjectPro

FEBRUARY 16, 2023

Whether you’re looking to track objects in a video stream, build a face recognition system, or edit images creatively, OpenCV Python implementation is the go-to choice for the job. At the core of such applications lies the science of machine learning, image processing, computer vision, and deep learning. What is OpenCV Python?

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Python Process Deep Learning Algorithm 98

ProjectPro

JANUARY 15, 2021

And honestly, there are a lot of real-world machine learning datasets around you that you can opt to start practicing your fundamental data science and machine learning skills, even without having to complete a comprehensive data science or machine learning course. Table of Contents What is a dataset in machine learning?

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Datasets Machine Learning Retail Banking 40

Cloudera

FEBRUARY 8, 2022

Cloudera has been recognized as a Visionary in 2021 Gartner® Magic Quadrant for Cloud Database Management Systems (DBMS) and for the first time, evaluated CDP Operational Database (COD) against the 12 critical capabilities for Operational Databases. Critical Capabilities for Cloud Database Management Systems for Operational Use Cases .

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Database Systems Cloud Management 67

RandomTrees

APRIL 12, 2024

In this blog, we delve into the intricacies of model retraining, exploring its significance, various approaches, triggers, and best practices to empower organizations in mastering this essential component of MLOps. What is Model Retraining? Why Retrain Models?

Machine Learning 52

Machine Learning Datasets Systems Process 52

Cloudera

DECEMBER 7, 2020

Apache Hadoop Distributed File System (HDFS) is the most popular file system in the big data world. The Apache Hadoop File System interface has provided integration to many other popular storage systems like Apache Ozone, S3, Azure Data Lake Storage etc. Migrating file systems thus requires a metadata update. .

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Systems Hadoop Metadata Datasets 59

Knowledge Hut

APRIL 25, 2024

In this blog post, we will discuss such technologies. If you pursue the MSc big data technologies course, you will be able to specialize in topics such as Big Data Analytics, Business Analytics, Machine Learning, Hadoop and Spark technologies, Cloud Systems etc. Spark is a fast and general-purpose cluster computing system.

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Big Data Technology NoSQL Hadoop 98

DataKitchen

MAY 10, 2024

This blog post delves into the third critical use case for Data Observation and Data Quality Validation: development and Deployment. The Fourth of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production.

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Data Ingestion Datasets Coding Data 49

ProjectPro

JANUARY 25, 2022

Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things. Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. Why use PySpark?

Big Data 52

Big Data Data Process Process Kafka 52

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. Download the complimentary 2023 Gartner Magic Quadrant for Cloud Database Management Systems report. Unlike software, ML models need continuous tuning.

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Cloud Unstructured Data Metadata Datasets 104

Pinterest Engineering

JULY 25, 2023

Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary. As businesses grow, so does the variety of these datasets and the complexity of their handling requirements.

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Big Data Accessible Accessibility Hadoop 95

Pinterest Engineering

JANUARY 4, 2024

In 2020, anticipating the growing needs of the business and to simplify our storage offerings, we decided to consolidate our different key-value systems in the company into a single unified service called KVStore. Maintaining these disparate systems and building common functionality among them was adding a huge overhead to the teams.

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Database Building Datasets Relational Database 78

Databand.ai

AUGUST 30, 2023

It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making. Consistency: The uniformity of data across different sources or systems. Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset.

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Data Cleanse Metadata High Quality Data Datasets 52

LinkedIn Engineering

DECEMBER 20, 2023

A key part of this work is our content abuse defense systems, which function behind the scenes to help detect and remove policy-violating content from the platform, while helping surface professional, relevant, and meaningful content that adds value to the member experience. What is AutoML?

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Machine Learning Datasets Algorithm Architecture 90

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. The greater the claim made using analytics, the greater the scrutiny on the process should be.

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Data Engineering Data Engineer Data Process Process 183

Workfall

JULY 18, 2023

As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable. In this blog, we’ll whisk you away on an enchanting journey through DBT materializations. In this blog, we will cover: What is DBT?

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Datasets Entertainment Data Workflow Data 77

Pinterest Engineering

SEPTEMBER 26, 2023

We have published a detailed blog post of its modeling architecture. The process is captured in Figure 1. The frontend logging system tracks the Pins that have been impressed by the Pinner and keeps a low percentage of the impressions and all positive engagements.

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Software Engineer Software Engineering Machine Learning Datasets 91

Netflix Tech

NOVEMBER 13, 2023

In this blog post, we will introduce speech and music detection as an enabling technology for a variety of audio applications in Film & TV, as well as introduce our speech and music activity detection (SMAD) system which we recently published as a journal article in EURASIP Journal on Audio, Speech, and Music Processing.

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Datasets Metadata Algorithm Architecture 89

DataKitchen

MAY 10, 2024

Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset.

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Raw Data Data Ingestion Datasets Data 49

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Effective Data Anomaly Monitoring (#2) Introduction Ensuring the accuracy and timeliness of data ingestion is a cornerstone for maintaining the integrity of data systems. This process is critical as it ensures data quality from the onset.

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Data Ingestion Transportation High Quality Data Data Schemas 52

Knowledge Hut

APRIL 23, 2024

While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. In this blog we will explore the fundamental differences between data warehouse and big data, highlighting their unique characteristics and benefits. Big data offers several advantages.

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Data Warehouse Big Data Unstructured Data Hadoop 52