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 17

Management Data Ingestion Data Collection Government 17

Towards Data Science

DECEMBER 4, 2023

Data Lake A data lake would serve as a repository for raw and unstructured data generated from various sources within the Formula 1 ecosystem: telemetry data from the cars (e.g. Data Lake & Data Integration We’ll face our first challenge while we integrate and consolidate everything in a single place.

Data Engineer 98

Data Engineer Data Engineering Engineering Data Lake 98

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Cloudera

NOVEMBER 22, 2021

Explosion of data availability from a variety of sources, including on-premises data stores used by enterprise data warehousing / data lake platforms, data on cloud object stores typically produced by heterogenous, cloud-only processing technologies, or data produced by SaaS applications that have now evolved into distinct platform ecosystems (e.g.,

Hadoop 94

Hadoop Government Data Security Cloud 94

Hevo

JUNE 26, 2024

Organizations deal with data collected from multiple sources, which increases the complexity of managing and processing it. Oracle offers a suite of tools that helps you store and manage the data, and Apache Spark enables you to handle large-scale data processing tasks.

Data Collection 52

Data Collection Data Data Process Process 52

Confluent

FEBRUARY 6, 2019

While all these solutions help data scientists, data engineers and production engineers to work better together, there are underlying challenges within the hidden debts: Data collection (i.e., integration) and preprocessing need to run at scale. Apache Kafka and KSQL for data scientists and data engineers.

Machine Learning 107

Machine Learning Python Kafka Java 107

Knowledge Hut

JULY 26, 2023

What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods. The real-time or near-real-time nature of Big Data poses challenges in capturing and processing data rapidly.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Striim

OCTOBER 11, 2024

While legacy ETL has a slow transformation step, modern ETL platforms, like Striim, have evolved to replace disk-based processing with in-memory processing. This advancement allows for real-time data transformation , enrichment, and analysis, providing faster and more efficient data processing.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. This approach ensures that only processed and refined data is housed in the data warehouse, leaving the raw data outside of it.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

AltexSoft

MAY 12, 2022

Audio data transformation basics to know. Before diving deeper into processing of audio files, we need to introduce specific terms, that you will encounter at almost every step of our journey from sound data collection to getting ML predictions. One of the largest audio data collections is AudioSet by Google.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

Striim

JULY 10, 2024

Here’s the process. Data Collection and Integration: Data is gathered from various sources, including sensor and IoT data, transportation management systems, transactional systems, and external data sources such as economic indicators or traffic data.

Management 52

Management Transportation Machine Learning High Quality Data 52

Knowledge Hut

JULY 3, 2023

Use Cases of Real-time Ingestion Real-time ingestion provides organizations with infrastructure for implementing various data capture, data processing and data analyzing tools. Here are some key uses of real-time data ingestion: 1. This process requires data integration tools and APIs for seamless connections.

Data Ingestion 52

Data Ingestion Google Cloud Pipeline-centric Media 52

Databand.ai

AUGUST 30, 2023

In a world where organizations rely heavily on data observability for informed decision-making, effective data testing methods are crucial to ensure high-quality standards across all stages of the data lifecycle—from data collection and storage to processing and analysis.

Data Validation 52

Data Validation Data Integration Data Database 52

ThoughtSpot

OCTOBER 18, 2024

Our Solution: Custom Build Analytics To address the challenges and limitations in our build management process, we developed a multi-step Build Analytics solution using ThoughtSpot as the central platform. Step 3: Implementing a data pipeline To automate the data collection and processing, we integrated a Jenkins job that runs hourly.

Building 52

Building Process Pipeline-centric Database-centric 52

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Knowledge Hut

JANUARY 19, 2024

However, having a lot of data is useless if businesses can't use it to make informed, data-driven decisions by analyzing it to extract useful insights. Business intelligence (BI) is becoming more important as a result of the growing need to use data to further organizational objectives.

Business Intelligence 52

Business Intelligence BI Business Analyst Finance 52

U-Next

AUGUST 17, 2022

Data Engineer roles and responsibilities have certain important components, such as: Refining the software development process using industry standards. Identifying and fixing data security flaws to shield the company from intrusions. Employing data integration technologies to get data from a single domain.

Data Engineer 52

Data Engineer Data Engineering Database-centric Pipeline-centric 52

RandomTrees

SEPTEMBER 10, 2024

AI enhances predictive maintenance in several ways: Data Analysis: In real-time modes, AI processes large volumes of information while detecting any patterns or anomalies that could indicate an impending failure ahead of traditional monitoring systems. AI algorithms can be used to access this data to start its analysis.

Transportation 59

Transportation Manufacturing Machine Learning Algorithm 59

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Volume Big Data refers to large volumes of data, typically in the order of terabytes or petabytes. It involves processing and analyzing massive datasets that cannot be managed with traditional data processing techniques. What Should You Choose Between Big Data and Small Data?

Big Data 52

Big Data Datasets Data Analysis Media 52

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

DECEMBER 21, 2023

Blockchain data-based cloud data integrity protection mechanism The "Blockchain data-based cloud data integrity protection mechanism" paper suggests a method for safeguarding the integrity of cloud data and which is one of the Cloud computing research topics.

Cloud Computing 52

Cloud Computing Cloud Algorithm Healthcare 52

AltexSoft

MAY 14, 2021

Big Data analytics processes and tools. Data ingestion. The process of identifying the sources and then getting Big Data varies from company to company. It’s worth noting though that data collection commonly happens in real-time or near real-time to ensure immediate processing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Knowledge Hut

JUNE 20, 2023

From blockchain-based database systems to real-time data processing with in-memory databases, these topics offer a glimpse into the exciting future of database research. Once data has been added to such a database, it cannot be modified or deleted. Relational databases, with their inherent structure, aid in this process.

Database 52

Database Java Education Data Collection 52

AltexSoft

MAY 12, 2023

Without a fixed schema, the data can vary in structure and organization. File systems, data lakes, and Big Data processing frameworks like Hadoop and Spark are often utilized for managing and analyzing unstructured data. The process requires extracting data from diverse sources, typically via APIs.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

AltexSoft

FEBRUARY 11, 2023

Sample of a high-level data architecture blueprint for Azure BI programs. Source: Pragmatic Works This specialist also oversees the deployment of the proposed framework as well as data migration and data integration processes. In some locations, this certification can be acquired online.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Ascend.io

SEPTEMBER 14, 2023

Integration with Spark: When paired with platforms like Spark, Python’s performance is further amplified. PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. getOrCreate() data = spark.read.csv("big_data.csv") data.groupBy("category").count().show()

Data Engineer 52

Data Engineer Data Engineering Python Engineering 52

Knowledge Hut

JUNE 23, 2023

Who Uses Real-time Data Analytics? Many industries and businesses utilize real-time data analytics to get insights and make decisions based on data collected in real time. Rapid ongoing data processing can be necessary for real-time data analytics.

Data Analytics 52

Data Analytics IT Transportation Analytics Architecture 52

Knowledge Hut

MARCH 27, 2024

Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses. Some important big data processing platforms are: Microsoft Azure. Why Is Big Data Analytics Important? Let's check some of the best big data analytics tools and free big data analytics tools.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

The Modern Data Company

APRIL 15, 2024

Transforming Data Complexity into Strategic Insight At first glance, the process of transforming raw data into actionable insights can seem daunting. The journey from data collection to insight generation often feels like operating a complex machine shrouded in mystery and uncertainty.

Building 52

Building Raw Data Food Data 52

Ascend.io

JULY 28, 2023

Now, you might ask, “How is this different from data stack architecture, or data architecture?” ” Data Stack Architecture : Your data stack architecture defines the technology and tools used to handle data, like databases, data processing platforms, analytic tools, and programming languages.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Knowledge Hut

JUNE 28, 2023

Business Intelligence: Business Intelligence can handle moderate to large volumes of structured data. While it may not be designed specifically for big data processing, it can integrate with data processing technologies to analyze substantial amounts of data.

Data Mining 52

Data Mining Business Intelligence BI Structured Data 52

Monte Carlo

FEBRUARY 21, 2023

Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing. Ducati’s advantage lies in the volumes of valuable performance data that help drive innovation. And this innovation ultimately creates bikes that the competition can only dream of.”

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

Reduced reliance on IT Integral to a data fabric is a set of pre-built models and algorithms that expedite data processing. Ducati’s advantage lies in the volumes of valuable performance data that help drive innovation. And this innovation ultimately creates bikes that the competition can only dream of.”

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Ascend.io

JANUARY 2, 2024

Actions: Establish connections to your data sources like CRM systems or social media platforms. Implement processes to validate and clean incoming data, such as verifying data formats or removing duplicates 3. Objective: Refine the data through specific transformations to make it suitable for analysis.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

Knowledge Hut

DECEMBER 29, 2023

Learning Outcomes: You will understand the processes and technology necessary to operate large data warehouses. Engineering and problem-solving abilities based on Big Data solutions may also be taught. Additionally, you will learn how to design and manage data processing systems.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

JUNE 28, 2016

Kafka is extensively being used across industries for general – purpose messaging system where high availability and real time data integration and analytics are of utmost importance.

Kafka 40

Kafka Systems Hadoop Big Data 40