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 109

Data Science Cloud Hadoop Metadata 109

Cloudera

MARCH 3, 2022

Data users in these enterprises don’t know how data is derived and lack confidence in whether it’s the right source to use. . If data access policies and lineage aren’t consistent across an organization’s private cloud and public clouds, gaps will exist in audit logs. From Bad to Worse.

Data Governance 96

Data Governance Government Metadata Medical 96

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

Data Engineering Weekly

FEBRUARY 18, 2024

A recent survey from dbt shows that 43% of the respondents answered that ambiguous data ownership was their biggest challenge. The author writes about the basic construct of data ownership and how to utilize dbt to reduce friction. I believe the data ownership problem is much deeper than simple metadata management.

Data Engineering 62

Data Engineering Data Engineer Engineering Data 62

Cloudera

MARCH 14, 2023

We just announced the general availability of Cloudera DataFlow Designer , bringing self-service data flow development to all CDP Public Cloud customers. In our previous DataFlow Designer blog post , we introduced you to the new user interface and highlighted its key capabilities.

Data Pipeline 81

Data Pipeline Designing Kafka Metadata 81

Cloudera

JUNE 18, 2022

In order to realize the benefits of both worlds — flexibility of analytics in data lakes, and simple and fast SQL in data warehouses — companies often deployed data lakes to complement their data warehouses, with the data lake feeding a data warehouse system as the last step of an extract, transform, load (ETL) or ELT pipeline.

Data Lake 90

Data Lake Data Warehouse BI SQL 90

DataKitchen

AUGUST 9, 2021

The domain also includes code that acts upon the data, including tools, pipelines, and other artifacts that drive analytics execution. The domain requires a team that creates/updates/runs the domain, and we can’t forget metadata: catalogs, lineage, test results, processing history, etc., …. DataOps Meta-Orchestration.

Pharmaceutical 98

Pharmaceutical Raw Data Data Lake Data 98

AltexSoft

FEBRUARY 11, 2023

If you are not familiar with the above-mentioned concepts, we suggest you to follow the links above to learn more about each of them in our blog posts. Bad data management be like, Source: Makeameme Data architects are sometimes confused with other roles inside the data science team. Problem-solving skills.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Netflix Tech

OCTOBER 19, 2020

which is difficult when troubleshooting distributed systems. In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. We could also get contextual information about the streaming session by joining relevant traces with account metadata and service logs.

Building 93

Building Transportation Metadata Java 93

Maxime Beauchemin

JANUARY 20, 2017

In relation to previously existing roles , the data engineering field could be thought of as a superset of business intelligence and data warehousing that brings more elements from software engineering. 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

Data Engineering Weekly

NOVEMBER 6, 2022

A data contract is a continuous and collaborative system because the business context and requirements won’t be static. I plan to write a series of blogs on Schemata and Data Contract in the coming weeks. Martin kindly stepped in for me to give the update for my promised blog posts.

Data Engineering 40

Data Engineering Data Engineer Engineering Machine Learning 40

Data Engineering Podcast

JUNE 29, 2020

This was a great conversation about the complexities of working in a niche domain of data analysis and how to build a pipeline of high quality data from collection to analysis. Can you describe how your overall data management system is architected? Can you describe how your overall data management system is architected?

Data Collection 100

Data Collection Management High Quality Data Metadata 100

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

LinkedIn Engineering

DECEMBER 12, 2023

Although we can prescribe a way to achieve an overall privacy guarantee with differential privacy with various algorithms, there is still the challenge of integrating differential privacy into an existing system that can return analytics for streaming data in real-time under intense query loads.

Algorithm 77

Algorithm Metadata SQL Datasets 77

Knowledge Hut

OCTOBER 26, 2023

In this blog, we will show some interesting AWS project ideas for all professionals, including beginners, intermediate, and advanced. blog) easily using any of your preferred CMS. Content Recommendation System You can build a content recommendation system using Amazon SageMaker, a machine learning service offered by AWS.

AWS 52

AWS Coding Project Cloud Computing 52

Databand.ai

JUNE 28, 2023

“Observability” has become a bit of a buzzword so it’s probably best to define it: Data observability is the blanket term for monitoring and improving the health of data within applications and systems like data pipelines. Data observability vs. monitoring: what is the difference?

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Cloudera

JANUARY 5, 2023

With the right tools in place, distilling actionable insights from data to achieve business objectives or unlock new revenue streams is easily achievable for organizations of all sizes across industries, especially with the availability of self-serve functionalities that do not require specialized ops or cloud expertise.

Banking 52

Banking Machine Learning Insurance Data Architecture 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

Cloudera

SEPTEMBER 17, 2020

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. .

Unstructured Data 80

Unstructured Data Data Warehouse Pharmaceutical MySQL 80

LinkedIn Engineering

JANUARY 19, 2024

Data consistency, feature reliability, processing scalability, and end-to-end observability are key drivers to ensuring business as usual (zero disruptions) and a cohesive customer experience. With our new data processing framework, we were able to observe a multitude of benefits, including 99.9%

Recruitment 88

Recruitment Data Process Process Kafka 88

Knowledge Hut

JULY 26, 2023

Database applications also help in data-driven decision-making by providing data analysis and reporting tools. In this blog, we will deep dive into database system applications in DBMS, and their components and look at a list of database applications. What are Database Applications? Columnar Database (e.g.-

Database 52

Database NoSQL Telecommunication MongoDB 52

Knowledge Hut

OCTOBER 26, 2023

In the world of machine learning, where data-driven solutions have the power to transform industries and empower individuals, if you're new to this exciting field and eager to embark on your machine-learning journey, you're in the right place. What is Machine Learning, and why are ML projects interesting?

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

Cloudera

AUGUST 30, 2018

Cloud service architecture – storing and accessing data and services over the Internet instead of your data center or local computer’s hard drive. Machine learning driven business – A focus on the design of systems that can learn from and make decisions and predictions based on data.

Unstructured Data 40

Unstructured Data Machine Learning Data Warehouse BI 40

Rockset

JUNE 7, 2021

Change data capture (CDC) is the process of recognising when data has been changed in a source system so a downstream process or system can action that change. A common use case is to reflect the change in a different target system so that the data in the systems stay in sync.

IT 40

IT Kafka Database MongoDB 40

Databand.ai

AUGUST 30, 2023

The importance of data quality cannot be overstated, as poor-quality data can result in incorrect conclusions, inefficient operations, and a lack of trust in the information provided by a company’s systems. Completeness: The extent that all required data is present and available.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. Late arriving facts Late arriving facts can be problematic with a strict immutable data policy.

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183

Zalando Engineering

NOVEMBER 29, 2017

In this blog post, we share our experiences and lessons learned to run our real-time applications built with Kafka’s Streams API in production on Amazon Web Services (AWS). Therefore for any of the affected applications, we introduced a processing timeout, ensuring there was an upper bound on the time taken by any of our edge cases.

Kafka 40

Kafka AWS Amazon Web Services Utilities 40

Rockset

MARCH 18, 2024

That’s because successfully deploying an AI application requires retrieval augmented generation or “RAG” pipelines, processing real-time data streams, chunking data, generating embeddings, storing embeddings and running vector search. These additional inputs are referred to as metadata filtering. What is RAG?

Cloud 64

Cloud Building Metadata Kafka 64

ProjectPro

JULY 23, 2021

AWS (Amazon Web Services) is the world’s leading and widely used cloud platform, with over 200 fully featured services available from data centers worldwide. This blog presents some of the most unique and innovative AWS projects from beginner to advanced levels. Real-time Data Processing Application 7.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

Big Data 78

Big Data Data Management Management Metadata 78

Netflix Tech

DECEMBER 3, 2022

The dataflow migration command is a special feature, developed single handedly by Stephen Huenneke , to fully automate the communication and tracking of a data warehouse table changes. All the above commands are very likely to be described in separate future blog posts, but right now let’s focus on the dataflow sample command.

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

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

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

Netflix Tech

DECEMBER 21, 2020

On the other hand, these optimizations themselves need to be sufficiently inexpensive to justify their own processing cost over the gains they bring. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

AltexSoft

NOVEMBER 7, 2022

Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. Metadata database. By default, Airflow handles metadata with SQLite which is meant for development only.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes. What is Data in Use?

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

Cloudera

AUGUST 16, 2021

CDF-PC enables Apache NiFi users to run their existing data flows on a managed, auto-scaling platform with a streamlined way to deploy NiFi data flows and a central monitoring dashboard making it easier than ever before to operate NiFi data flows at scale in the public cloud. The need for a cloud-native Apache NiFi service.

Cloud 118

Cloud Unstructured Data Utilities Metadata 118