Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

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. Finally, you'll find a list of PySpark projects to help you gain hands-on experience and land an ideal job in Data Science or Big Data.

Big Data 52

Big Data Data Process Process Kafka 52

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Datasets 98

DataKitchen

NOVEMBER 5, 2024

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. By systematically moving data through these layers, the Medallion architecture enhances the data structure in a data lakehouse environment.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

Striim

JUNE 6, 2024

Conversely, high latency can hinder your organization’s data integration and streaming efforts. As data-driven decision-making becomes increasingly vital, the importance of minimizing latency has never been clearer. The way that you can do so is by harnessing real-time data processing over batch processing methodologies.

Data Integration 52

Data Integration Data Ingestion Healthcare Data Pipeline 52

Cloudera

FEBRUARY 15, 2024

Data integration and ingestion: With robust data integration capabilities, a modern data architecture makes real-time data ingestion from various sources—including structured, unstructured, and streaming data, as well as external data feeds—a reality.

Insurance 87

Insurance Banking Data Architecture Data Ingestion 87

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Contents: What is the role of an Azure Data Engineer? Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Edureka

AUGUST 23, 2024

The AWS training will prepare you to become a master of the cloud, storing, processing, and developing applications for the cloud data. Amazon AWS Kinesis makes it possible to process and analyze data from multiple sources in real-time. What can I do with Kinesis Data Streams? How Amazon Kinesis Works?

AWS 52

AWS Kafka Amazon Web Services Medical 52

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs.

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

Data Engineering Weekly

MAY 16, 2023

In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive? I won’t bore you with the importance of data quality in the blog. Let’s talk about the data processing types.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Database 40

Databand.ai

JULY 17, 2023

Aim to automate processes: Automation is a key aspect of both DataOps and MLOps as it helps streamline workflows, reduce errors, increase efficiency, and ensure consistency across projects. Better data observability equals better data quality.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Christophe Blefari

JANUARY 20, 2024

He wrote some years ago 3 articles defining data engineering field. Some concepts When doing data engineering you can touch a lot of different concepts. The main difference between both is the fact that your computation resides in your warehouse with SQL rather than outside with a programming language loading data in memory.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Snowflake

AUGUST 19, 2024

In this blog, we will cover some of the most recently launched improvements for the Snowflake platform. For example: Ingest performance: We improved the ingest performance of both JSON and Parquet files with case-insensitive data up to 25%. .* 25, 2022 to April 30, 2024.

BI 52

BI Data Ingestion Structured Data Engineering 52

Pinterest Engineering

OCTOBER 23, 2024

In this blog, we’ll be covering the challenges and corresponding solutions that enable us to migrate thousands of Spark applications from Monarch to Moka while maintaining resource efficiency and a high quality of service. At the time of writing this blog, we have migrated half of the Spark workload running on Monarch to Moka.

AWS 52

AWS Hadoop Management Algorithm 52

Data Engineering Weekly

NOVEMBER 20, 2022

[link] The short YouTube video gives a nice overview of the Data Cards. We often think of AI/ ML as a complex data processing problem, but it doesn’t make any use until it is exposed to an end user or an application. The blog narrates one such application that uses video quality with neural networks.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Databand.ai

AUGUST 30, 2023

Automation plays a critical role in the DataOps framework, as it enables organizations to streamline their data management and analytics processes and reduce the potential for human error. This can be achieved through the use of automated data ingestion, transformation, and analysis tools.

Data Governance 52

Data Governance Data Pipeline Government Business Analyst 52

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. . Conclusion.

Manufacturing 108

Manufacturing Data Warehouse Kafka Retail 108

Cloudera

FEBRUARY 8, 2021

This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle.

Data Pipeline 117

Data Pipeline Building Manufacturing Data Warehouse 117

LinkedIn Engineering

JUNE 15, 2023

In this blog post, we will discuss the AvroTensorDataset API, techniques we used to improve data processing speeds by up to 162x over existing solutions (thereby decreasing overall training time by up to 66%), and performance results from benchmarks and production.

Datasets 102

Datasets Bytes Process Data Ingestion 102

Data Engineering Weekly

SEPTEMBER 11, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. The blog narrates the key concepts of the Kimball model and a modern outlook on the concepts.

Data Engineering 62

Data Engineering Data Engineer Engineering Kafka 62

Data Science Blog: Data Engineering

AUGUST 22, 2024

Tools like BiG EVAL are leading data quality field for all technical systems in which data is transported and transformed. Real-Time Data Processing Businesses are adopting technologies that can process and analyze data instantly due to the need for real-time insights.

Data Preparation 52

Data Preparation Transportation High Quality Data Data Science 52

Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

Google Cloud 52

Google Cloud Data Workflow Data Data Engineering 52

phData: Data Engineering

NOVEMBER 8, 2024

Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. In this blog, we will discuss: What is the Open Table format (OTF)? Why should we use it? A Brief History of OTF A comparative study between the major OTFs.

Architecture 52

Architecture Systems Data Lake Google Cloud 52

Knowledge Hut

APRIL 23, 2024

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Analytics: Both data warehousing and big data platforms enable analytical capabilities.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Monte Carlo

JUNE 2, 2024

These languages are used to write efficient, maintainable code and create scripts for automation and data processing. Databases and Data Warehousing: Engineers need in-depth knowledge of SQL (88%) and NoSQL databases (71%), as well as data warehousing solutions like Hadoop (61%).

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

These languages are used to write efficient, maintainable code and create scripts for automation and data processing. Databases and Data Warehousing: Engineers need in-depth knowledge of SQL (88%) and NoSQL databases (71%), as well as data warehousing solutions like Hadoop (61%).

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Ascend.io

OCTOBER 28, 2024

Data transformation helps make sense of the chaos, acting as the bridge between unprocessed data and actionable intelligence. You might even think of effective data transformation like a powerful magnet that draws the needle from the stack, leaving the hay behind.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

LinkedIn Engineering

OCTOBER 19, 2023

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119

Process Lambda Architecture Kafka Machine Learning 119

Cloudera

JUNE 7, 2021

It calls out that Cloudera DataFlow “ includes streaming flow and streaming data processing unified with Cloudera Data Platform ”. The post Cloudera named a Strong Performer in The Forrester Wave™: Streaming Analytics, Q2 2021 appeared first on Cloudera Blog.

Kafka 103

Kafka Data Ingestion Cloud Architecture 103

ProjectPro

FEBRUARY 6, 2023

Data engineers often use Google Cloud Pub/Sub to design asynchronous workflows, publish event notifications, and stream data from several processes or devices. This blog provides an overview of Google Cloud Pub/Sub that will help you understand the framework and its suitable use cases for your data engineering projects.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

DataKitchen

OCTOBER 20, 2023

The Challenge: High Stakes in the Age of Personalized Data Observability The primary challenge stems from the requirement of Data Consumers for personalized monitoring and alerts based on their unique data processing needs. Data Observability platforms often need to deliver this level of customization.

Insurance 69

Insurance Pharmaceutical Data Data Ingestion 69

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

Knowledge Hut

MARCH 19, 2024

I will explore the top 10 AWS applications and their use cases in this blog. Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks. 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

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107