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 49

Raw Data Data Ingestion Datasets Data 49

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 86

Insurance Banking Data Architecture Data Ingestion 86

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

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 Datasets 40

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

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

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

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 Data Ingestion 52

Cloudera

AUGUST 26, 2021

In addition to big data workloads, Ozone is also fully integrated with authorization and data governance providers namely Apache Ranger & Apache Atlas in the CDP stack. While we walk through the steps one by one from data ingestion to analysis, we will also demonstrate how Ozone can serve as an ‘S3’ compatible object store.

Data Science 111

Data Science Cloud Hadoop Metadata 111

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

Snowflake

MARCH 21, 2023

In February, Snowflake launched new features around streaming data ingestion and data governance and improved SQL experience and performance, with enhancements to Search Optimization Service and more. Check out Felipe Hoffa’s video on how to use Snowsight to get from data to decision faster.

Retail 72

Retail Healthcare Data Ingestion Consulting 72

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 Data Cleanse 52

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

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

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

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 107

Manufacturing Data Warehouse Kafka Retail 107

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 116

Data Pipeline Building Manufacturing Data Warehouse 116

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

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

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

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

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 102

Kafka Data Ingestion Architecture Cloud 102

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 67

Insurance Pharmaceutical Data Data Ingestion 67

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

Cloudera

JUNE 16, 2022

Use cases like fraud detection, network threat analysis, manufacturing intelligence, commerce optimization, real-time offers, instantaneous loan approvals, and more are now possible by moving the data processing components up the stream to address these real-time needs. . Faster data ingestion: streaming ingestion pipelines.

Kafka 86

Kafka Manufacturing Data Lake SQL 86

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Cloudera partners are also benefiting from Apache Iceberg in CDP. ORC open file format support.

Cloud 78

Cloud Metadata Google Cloud Data Warehouse 78

Cloudera

JULY 21, 2022

The table below summarizes Hive and Druid key features and strengths and suggests how combining the feature sets can provide the best of both worlds for data analytics. Cloudera Data Warehouse). Efficient batch data processing. Complex data transformations. Native streaming ingestion support from Kafka and Kinesis.

BI 85

BI Digital Media Data Warehouse Kafka 85

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 106

Machine Learning Python Kafka Java 106

Ascend.io

JUNE 6, 2023

In this blog post, we delve deep into the factors that contribute to how Ascend runs in the cloud, focusing on the primary areas that contribute to costs in this context: storage, compute, networking, and retries. Anytime data processing encounters a failure for a variety of reasons, retries commonly resolve the problem.

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

DareData

JULY 5, 2023

In this blog post, we aim to share practical insights and techniques based on our real-world experience in developing data lake infrastructures for our clients - let's start! Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Snowflake

SEPTEMBER 11, 2023

As Snowflake streams define an offset to track change data capture (CDC) changes on underlying tables and views, Tasks can be used to schedule the consumption of that data. We covered this in depth in a previous blog post. Today’s Snowflake Dynamic Tables do not support append-only data processing.

SQL 71

SQL Data Raw Data Architecture 71

ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? Why Are Data Engineering Skills In Demand? Don’t worry!

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 28, 2024

This demonstrates the increasing need for Microsoft Certified Data Engineers. In this blog, I will explore Azure data engineer jobs and the top 10 job roles in this field where you can begin your career. Implement data ingestion, processing, and analysis pipelines for large-scale data sets.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Knowledge Hut

SEPTEMBER 29, 2023

Moreover, what benefits can you expect from a career in Azure Data Engineering? This blog aims to answer these questions, providing a straightforward and professional insight into the world of Azure Data Engineering. Join us on this journey through the exciting realm of Azure Data Engineering.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. This flexibility allows tracer libraries to record 100% traces in our mission-critical streaming microservices while collecting minimal traces from auxiliary systems like offline batch data processing.

Building 93

Building Transportation Metadata Java 93

Cloudera

SEPTEMBER 7, 2023

By embedding intelligence at the data platform-level, it becomes possible to accelerate the pace at which financial institutions can operationalize AI solutions. The combination of built-in data management and governance capabilities provides a solid foundation for firms to embed Trusted AI across their operations.

Insurance 81

Insurance Finance Portfolio Government 81

Knowledge Hut

APRIL 25, 2023

Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases. Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52