Striim

JULY 10, 2024

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. Here’s the process. That’s where Striim came into play.

Management 52

Management Transportation Machine Learning High Quality Data 52

Christophe Blefari

JANUARY 20, 2024

In a nutshell you have: text based formats (CSV, JSON and raw stuff), columnar file formats (Parquet, ORC), memory format ( Arrow ), transport protocols and format (Protobuf, Thrift, gRPC, Avro), table formats ( Hudi, Iceberg, Delta ), database and vendor formats (Postgres, Snowflake, BigQuery, etc.).

Data Engineer 130

Data Engineer Data Engineering Engineering Hadoop 130

Cloudera

APRIL 20, 2021

Transportation: Monitor truck health and performance from smartphones and tablets, prioritize needed reports, and quickly identify the nearest dealer service locations. A modern streaming architecture consists of critical components that provide data ingestion, security and governance, and real-time analytics.

Kafka 94

Kafka Hospitality Retail Data Ingestion 94

Pinterest Engineering

NOVEMBER 7, 2023

Jeff Xiang | Software Engineer, Logging Platform Vahid Hashemian | Software Engineer, Logging Platform Jesus Zuniga | Software Engineer, Logging Platform At Pinterest, data is ingested and transported at petabyte scale every day, bringing inspiration for our users to create a life they love.

Kafka 110

Kafka Java Software Engineer Software Engineering 110

Cloudera

AUGUST 27, 2021

The organization was locked into a legacy data warehouse with high operational costs and inability to perform exploratory analytics. With more than 25TB of data ingested from over 200 different sources, Telkomsel recognized that to best serve its customers it had to get to grips with its data. .

Telecommunication 83

Telecommunication Transportation Big Data Data Ingestion 83

Netflix Tech

OCTOBER 19, 2020

Stream Processing: to sample or not to sample trace data? This was the most important question we considered when building our infrastructure because data sampling policy dictates the amount of traces that are recorded, transported, and stored. Mantis is our go-to platform for processing operational data at Netflix.

Building 94

Building Transportation Java Metadata 94

Snowflake

AUGUST 16, 2023

Data comes in a continuous manner, and often a separate architecture is required to handle streaming data. What remains challenging is how streaming data is brought together with batch data. That’s why we built Snowpipe Streaming, now generally available to handle row-set data ingestion. Commerce Datos Inc.

Transportation 52

Transportation Scala Kafka Data Lake 52

Cloudera

JANUARY 20, 2021

The data journey is not linear, but it is an infinite loop data lifecycle – initiating at the edge, weaving through a data platform, and resulting in business imperative insights applied to real business-critical problems that result in new data-led initiatives. Conclusion.

Manufacturing 108

Manufacturing Data Warehouse Kafka Retail 108

Data Science Blog: Data Engineering

AUGUST 22, 2024

This shift not only saves time but also ensures a higher standard of data quality. Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed.

Data Preparation 52

Data Preparation Transportation High Quality Data Data Science 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 89

Building Metadata Transportation Data Ingestion 89

Knowledge Hut

APRIL 25, 2024

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. Machine learning is increasingly becoming a necessary component of every workflow due to the growing quantity of data businesses gather, store, and evaluate.

Data Science 52

Data Science Telecommunication Transportation Big Data 52

Striim

JUNE 4, 2024

Contrary to traditional methods, such as batch processing where data is collected, stored, and analyzed at a later time, with real-time processing there’s no delay even for high-velocity data sets. This data must be ingested with minimal latency to ensure it is available for immediate processing.

Machine Learning 52

Machine Learning Algorithm Healthcare Utilities 52

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Data Engineering Weekly

JANUARY 29, 2023

The data observability solutions require more attention in the remediation workflow to ensure they don’t end up as a disjointed workflow like Data Catalogs. Meta: Tulip - Modernizing Meta’s data platform Meta writes about Tulip's adoption story, its data transportation, and the serialization protocol for its data platform.

Data Engineer 52

Data Engineer Data Engineering Engineering Deep Learning 52

Towards Data Science

JULY 8, 2024

ML Pipeline operations begins with data ingestion and validation, followed by transformation. The transformed data is trained and deployed. Initializing the InteractiveContext # This will create an sqlite db for storing the metadata context = InteractiveContext(pipeline_root=_pipeline_root) Next, we start with data ingestion.

Data Preparation 46

Data Preparation Datasets Metadata Data Ingestion 46

Striim

APRIL 1, 2024

Focused on delivering real-time intelligence for AI and leveraging change data capture (CDC) from databases, Striim’s approach addresses the urgent need for thorough data integration, emphasizing the critical role of connecting disparate applications to fully realize their potential. What is Striim Cloud for Application Integration?

Hospitality 52

Hospitality Transportation Google Cloud Cloud 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. Azure IoT by Microsoft is a comprehensive platform that helps organizations connect, monitor, and manage many IoT devices and assets. trillion by 2026.

Cloud Computing 52

Cloud Computing Utilities Cloud Transportation 52

Cloudera

MAY 28, 2019

What emerges is the criticality of a data strategy and core data management competency, including both data and model management, to support enterprise ML initiatives.

Data Science 50

Data Science Transportation Machine Learning Algorithm 50

Snowflake

MAY 22, 2023

Data Ingestion Snowpipe auto-ingest expands to support cross-cloud and cross-platform ingestion – public preview With this release, we are making a few enhancements to Snowpipe auto-ingest to make ingestion easier with Snowflake. Visit our documentation page to learn more.

Healthcare 52

Healthcare Medical Scala Transportation 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. Any option can pair well with Apache Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Silectis

JANUARY 3, 2021

Data Engineering Data engineering is a process by which data engineers make data useful. Data engineers design, build, and maintain data pipelines that transform data from a raw state to a useful one, ready for analysis or data science modeling. HDFS stands for Hadoop Distributed File System.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

Rockset

NOVEMBER 28, 2022

CDC leverages streaming in order to track and transport changes from one system to another. First, CDC theoretically allows companies to analyze and react to data in real time, as it’s generated. These will help users more easily configure the correct transformations on top of CDC data.

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB SQL 52

Monte Carlo

FEBRUARY 15, 2023

Yes, data warehouses can store unstructured data as a blob datatype. Data Transformation Raw data ingested into a data warehouse may not be suitable for analysis. Data engineers use SQL, or tools like dbt, to transform data within the data warehouse. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Knowledge Hut

JUNE 26, 2023

Finnhub API with Kafka for Real-Time Financial Market Data Pipeline Project Overview: The goal of this project is to construct a streaming data pipeline by making use of the real-time financial market data API provided by Finnhub.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Confluent

MAY 22, 2019

As IoT projects go from concepts to reality, one of the biggest challenges is how the data created by devices will flow through the system. Since MQTT is designed for low-power and coin-cell-operated devices, it cannot handle the ingestion of massive datasets. Interactive M2M/IoT Sector Map. MQTT Proxy + Apache Kafka (no MQTT broker).

Kafka 98

Kafka Google Cloud NoSQL Entertainment 98

ProjectPro

FEBRUARY 8, 2023

AWS Glue Studio offers several built-in transforms for the purpose of processing your data. A DynamicFrame, an extension of an Apache Spark SQL DataFrame, transports your data from one job node to the next. You can transform your data using the Transform-ApplyMapping transform node or additional transforms.

AWS 98

AWS Scala Metadata Data Lake 98

ProjectPro

DECEMBER 7, 2021

However, you can also pull data from centralized data sources like data warehouses to transform data further and build ETL pipelines for training and evaluating AI agents. Processing: It is a data pipeline component that decides the data flow implementation.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

MAY 14, 2021

It’s represented in terms of batch reporting, near real-time/real-time processing, and data streaming. The best-case scenario is when the speed with which the data is produced meets the speed with which it is processed. Let’s take the transportation industry for example. Big Data analytics processes and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

AUGUST 21, 2015

What are the steps involved in deploying a big data solution? The core components of Flume are – Event- The single log entry or unit of data that is transported. Source- This is the component through which data enters Flume workflows. Sink-It is responsible for transporting data to the desired destination.

Hadoop 40

Hadoop Big Data Metadata Java 40

Knowledge Hut

SEPTEMBER 29, 2023

Data engineers serve as the architects, laying the foundation upon which data scientists construct their projects. They are responsible for the crucial tasks of gathering, transporting, storing, and configuring data infrastructure, which data scientists rely on for analysis and insights.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

U-Next

SEPTEMBER 23, 2022

Here are some more instances of how businesses use Big Data: Big data assists oil and gas businesses in identifying potential drilling locations and monitoring pipeline operations; similarly, utilities use it to track power networks. . Data collection might be conditionally triggered, scheduled, or ad hoc. .

Big Data Ecosystem 40

Big Data Ecosystem Big Data Healthcare Data Lake 40

ProjectPro

MAY 31, 2021

Waste management involves the process of handling, transporting, storing, collecting, recycling, and disposing of the waste generated. This can be classified as a Big Data Apache project by using Hadoop to build it. Big Data Analytics Projects Solution for Visualization of Clickstream Data on a Website 21.

Big Data 52

Big Data Coding Project Hadoop 52

Monte Carlo

MARCH 28, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Rockset

DECEMBER 9, 2021

Lifting-and-shifting their big data environment into the cloud only made things more complex. The modern data stack introduced a set of cloud-native data solutions such as Fivetran for data ingestion, Snowflake, Redshift or BigQuery for data warehousing , and Looker or Mode for data visualization.

Transportation 52

Transportation BI SQL Database 52

Towards Data Science

APRIL 3, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Engineer 98

Data Engineer Data Engineering Engineering Data Warehouse 98