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

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 104

Kafka Data Ingestion Cloud Architecture 104

Striim

SEPTEMBER 11, 2024

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

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

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 89

Insurance Banking Data Architecture Data Ingestion 89

Snowflake

OCTOBER 16, 2024

For organizations who are considering moving from a legacy data warehouse to Snowflake, are looking to learn more about how the AI Data Cloud can support legacy Hadoop use cases, or are struggling with a cloud data warehouse that just isn’t scaling anymore, it often helps to see how others have done it.

Digital Media 82

Digital Media Media Data Lake Data Warehouse 82

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

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

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

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 81

Architecture Raw Data Pipeline-centric Data Ingestion 81

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

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

Maxime Beauchemin

JANUARY 20, 2017

The fact that ETL tools evolved to expose graphical interfaces seems like a detour in the history of data processing, and would certainly make for an interesting blog post of its own. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 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 95

Building Transportation Java Metadata 95

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

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 87

BI Digital Media Data Warehouse Kafka 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

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 53

Raw Data Data Ingestion Datasets Data 53

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 80

Cloud Metadata Data Warehouse Google Cloud 80

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

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. ® , Go, and Python SDKs where an application can use SQL to query raw data coming from Kafka through an API (but that is a topic for another blog). However, Apache Kafka is more than just messaging.

Kafka 21

Kafka SQL BI Hadoop 21

Netflix Tech

DECEMBER 21, 2020

Merge As the data lands into the data warehouse through real-time data ingestion systems, it comes in different sizes. Merging those numerous smaller files into a handful of larger files can make query processing faster and reduce storage space. We will publish a follow-up blog post about AutoAnalyze in the future.

Data Warehouse 84

Data Warehouse Metadata Algorithm Data 84

Confluent

OCTOBER 10, 2019

Most scenarios require a reliable, scalable, and secure end-to-end integration that enables bidirectional communication and data processing in real time. MQTT Proxy for data ingestion without an MQTT broker. But that doesn’t move much.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

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 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

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

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 72

Insurance Pharmaceutical Data Data Ingestion 72

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

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

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

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

Cloudera

AUGUST 31, 2021

Case Study: Accenture’s Experience on Legacy Data Warehouse Migration into Cloudera with a Health Insurance Company . Due to the high storage cost in the legacy EDW solution, 100% source data capture proved cost-prohibitive – this led to continuing and costly change cycles to load incremental source updates as business requirements changed.

Data Warehouse 83

Data Warehouse Database-centric Metadata Cloud 83

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

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

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 Data Pipeline Aggregated Data 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 73

SQL Data Raw Data Architecture 73