Knowledge Hut

MARCH 19, 2024

Amazon Kinesis Amazon Kinesis is a set of services completely managed and dedicated to real-time data streaming and analytics. It makes real-time streaming data collection, processing, and analytics possible for timely insight and decision-making for businesses.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Ascend.io

JANUARY 2, 2024

The sources of data can be incredibly diverse, ranging from data warehouses, relational databases, and web analytics to CRM platforms, social media tools, and IoT device sensors. Regardless of the source, data ingestion, which usually occurs in batches or as streams, is the critical first step in any data pipeline.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Monte Carlo

JULY 10, 2023

Data can go missing for nearly endless reasons, but here are a few of the most common challenges around data completeness: Inadequate data collection processes Data collection and data ingestion can cause data completion issues when collection procedures aren’t standardized, requirements aren’t clearly defined, and fields are incomplete or missing.

Data Collection 40

Data Collection Data Governance Government Data 40

Knowledge Hut

APRIL 23, 2024

It involves assessing the credibility and reputation of the sources from which the data is obtained. Data from trustworthy and reputable sources are more reliable and dependable. On the other hand, "methodology" refers to the techniques and procedures used for data collection, processing, and analysis.

Big Data 52

Big Data Media Datasets Unstructured Data 52

AltexSoft

JUNE 26, 2023

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. We’ll particularly explore data collection approaches and tools for analytics and machine learning projects. What is data collection?

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ascend.io

JANUARY 10, 2024

The goal is to ensure your organization has the capability to process and prepare data effectively for your AI models. Let’s dive into what this involves and how you can make it actionable in your own setting: Data Ingestion: First things first: getting the data into the system.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Knowledge Hut

MARCH 22, 2024

It includes the service and capability portfolio that makes the device connectivity, data ingestion, analytics, and integration with other cloud services. It also allows organizations to leverage data collected from IoT devices, converting IoT data into actionable information. trillion by 2026.

Cloud Computing 52

Cloud Computing Utilities Cloud Transportation 52

Monte Carlo

MARCH 24, 2023

We often refer to these issues as data freshness or stale data. For example: The source system could provide corrupt data or rows with excessive NULLs. A poorly coded data pipeline could introduce an error during the data ingestion phase as the data is being clean or normalized.

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Knowledge Hut

APRIL 25, 2023

An end-to-end Data Science pipeline starts from business discussion to delivering the product to the customers. One of the key components of this pipeline is Data ingestion. It helps in integrating data from multiple sources such as IoT, SaaS, on-premises, etc., What is Data Ingestion?

Data Ingestion 52

Data Ingestion Lambda Architecture Raw Data Kafka 52

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. integration) and preprocessing need to run at scale.

Machine Learning 106

Machine Learning Python Kafka Java 106

Netflix Tech

OCTOBER 19, 2020

We chose Mantis as our backbone to transport and process large volumes of trace data because we needed a backpressure-aware, scalable stream processing system. Our trace data collection agent transports traces to Mantis job cluster via the Mantis Publish library.

Building 93

Building Transportation Metadata Java 93

DareData

JULY 5, 2023

Users: Who are users that will interact with your data and what's their technical proficiency? Data Sources: How different are your data sources? Latency: What is the minimum expected latency between data collection and analytics? And what is their format?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Knowledge Hut

JUNE 26, 2023

Use Stack Overflow Data for Analytic Purposes Project Overview: What if you had access to all or most of the public repos on GitHub? As part of similar research, Felipe Hoffa analysed gigabytes of data spread over many publications from Google's BigQuery data collection. Which queries do you have?

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Cloudera

FEBRUARY 16, 2022

We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle. So, we have workspaces, projects and sessions in that order.

Machine Learning 81

Machine Learning Algorithm Government Metadata 81

Cloudera

MAY 9, 2019

Data readiness – These set of metrics help you measure if your organization is geared up to handle the sheer volume, variety and velocity of IoT data. It is meant for you to assess if you have thought through processes such as continuous data ingestion, enterprise data integration and data governance.

Manufacturing 53

Manufacturing Data Ingestion Architecture Data Governance 53

Knowledge Hut

APRIL 25, 2024

Only one in three data scientists claim to be specialist in geographical analysis, indicating that there are still very few spatial data scientists. Generally, five key steps comprise the standard workflow for spatial data scientists, which takes them from data collection to offering business insights after the process.

Data Science 52

Data Science Telecommunication Transportation Big Data 52

ProjectPro

FEBRUARY 21, 2023

Knowledge of the definition and architecture of AWS Big Data services and their function in the data engineering lifecycle, including data collection and ingestion, data analytics, data storage, data warehousing, data processing, and data visualization.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Cloudera

APRIL 15, 2019

While Cloudera Flow Management has been eagerly awaited by our Cloudera customers for use on their existing Cloudera platform clusters, Cloudera Edge Management has generated equal buzz across the industry for the possibilities that it brings to enterprises in their IoT initiatives around edge management and edge data collection.

Management 16

Management Data Ingestion Data Collection Government 16

Rockset

DECEMBER 17, 2020

Deploy a SQL Query as an API on Rockset Once we’ve connected our data sources and created data collections in Rockset, we can start writing queries. On Rockset, we can use SQL queries to extract meaningful insights from raw semi-structured data ingested without a predefined schema.

Building 40

Building Aggregated Data SQL Data Ingestion 40

Confluent

FEBRUARY 21, 2019

It caused me to wonder if there was anything that I could do with my project HELK to apply some of the relationships presented in our talk, and enrich the data collected from my endpoints in real time. Which one you use depends on the particular use to which you are putting the data.

Process 80

Process Kafka Datasets SQL 80

Cloudera

FEBRUARY 4, 2019

Cloudera DataFlow (CDF) is a scalable, real-time streaming data platform that collects, curates, and analyzes data so customers gain key insights for immediate actionable intelligence. CDF, as an end-to-end streaming data platform, emerges as a clear solution for managing data from the edge all the way to the enterprise.

Data Ingestion 72

Data Ingestion Retail Kafka Data Lake 72

Cloudera

FEBRUARY 8, 2021

To accomplish this, ECC is leveraging the Cloudera Data Platform (CDP) to predict events and to have a top-down view of the car’s manufacturing process within its factories located across the globe. . Having completed the Data Collection step in the previous blog, ECC’s next step in the data lifecycle is Data Enrichment.

Data Pipeline 114

Data Pipeline Building Manufacturing Data Warehouse 114

Ascend.io

MAY 31, 2023

Table of Contents The Common Threads: Ingest, Transform, Share Before we explore the differences between the ETL process and a data pipeline , let’s acknowledge their shared DNA. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Cloudera

NOVEMBER 19, 2020

Analyzing historical data is an important strategy for anomaly detection. The modeling process begins with data collection. Here, Cloudera Data Flow is leveraged to build a streaming pipeline which enables the collection, movement, curation, and augmentation of raw data feeds.

Government 115

Government Machine Learning Algorithm Raw Data 115

Data Engineering Weekly

NOVEMBER 20, 2022

Google AI: The Data Cards Playbook: A Toolkit for Transparency in Dataset Documentation Google published Data Cards , a dataset documentation framework aimed at increasing transparency across dataset lifecycles. With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Data Engineering Weekly

NOVEMBER 13, 2022

With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation. The blog narrates the design of the data collection, modeling & visualization layers.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Cloudera

DECEMBER 21, 2020

As customers shift online, the data trails they leave behind, through email opens, click-throughs, preferred member programs, can help retailers provide personalized insights on a level like never before.

Retail 88

Retail Cloud Food Government 88

Rockset

OCTOBER 26, 2022

We gained so much confidence in Rockset’s speed, scalability, and ease of use that we quickly moved the rest of our analytical operations to Rockset.

SQL 52

SQL NoSQL Database Aggregated Data 52

Netflix Tech

MARCH 25, 2019

As a result, a single consolidated and centralized source of truth does not exist that can be leveraged to derive data lineage truth. Therefore, the ingestion approach for data lineage is designed to work with many disparate data sources. push or pull. Today, we are operating using a pull-heavy model.

Building 87

Building Metadata Transportation Data Ingestion 87

Ascend.io

JULY 28, 2023

Data pipeline architecture is a framework that outlines the flow and management of data from its original source to its final destination within a system. This framework encompasses the steps of data ingestion, transformation, orchestration, and sharing.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Cloudera

JANUARY 11, 2021

NiFi offers a wide range of protocols — MQTT, Kafka Protocol, HTTP, Syslog, JDBC, TCP/UDP, and more — to interact with when it comes to ingesting data. NiFi is a great, consistent, and unique software to manage all your data ingestion. The most common protocol is HTTP.

Kafka 60

Kafka Data Cleanse Java Datasets 60

Monte Carlo

MAY 25, 2023

Data orchestration helps companies comply with various international privacy laws and regulations, many of which require companies to demonstrate the source and rationale for their data collection. As data volume grows, scheduling becomes critical to successfully managing your data ingestion and transformation jobs.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Knowledge Hut

JANUARY 18, 2024

Let us now look into the differences between AI and Data Science: Data Science vs Artificial Intelligence [Comparison Table] SI Parameters Data Science Artificial Intelligence 1 Basics Involves processes such as data ingestion, analysis, visualization, and communication of insights derived.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

U-Next

JUNE 29, 2022

Data ingestion can be divided into two categories: . A batch is a method of gathering and delivering huge data groups at once. Conditions can trigger data collection, scheduled or done on the fly. A constant flow of data is referred to as streaming. For real-time data analytics, this is required.

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Monte Carlo

FEBRUARY 15, 2023

This article will define in simple terms what a data warehouse is, how it’s different from a database, fundamentals of how they work, and an overview of today’s most popular data warehouses. What is a data warehouse? Yes, data warehouses can store unstructured data as a blob datatype. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

AltexSoft

SEPTEMBER 21, 2023

Logstash is a server-side data processing pipeline that ingests data from multiple sources, transforms it, and then sends it to Elasticsearch for indexing. Fluentd is a data collector and a lighter-weight alternative to Logstash. It is designed to unify data collection and consumption for better use and understanding.

Engineering 52

Engineering NoSQL Programming Language Java 52