Workfall

JUNE 12, 2023

Moreover, it facilitates the implementation of microservices architectures and event-driven systems, automating reactions to data changes without manual intervention. In real-time data streaming and event-driven architectures, CDC captures data changes to trigger actions or workflows.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

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 70

Insurance Pharmaceutical Data Data Ingestion 70

Data Engineering Podcast

APRIL 24, 2022

In this episode Andy Dang explains why the project was created, how you can apply it to your existing data systems, and how it functions to provide detailed context for being able to gain insight into all of your data processes. How do you structure the log events and metadata to provide detail and context for data applications?

Machine Learning 100

Machine Learning Systems Data Lake Metadata 100

Cloudera

MARCH 14, 2023

Once a draft has been created or opened, developers use the visual Designer to build their data flow logic and validate it using interactive test sessions. In the Designer, you have the ability to start and stop each step of the data pipeline, resulting in events being queued up in the connections that link the processing steps together.

Data Pipeline 81

Data Pipeline Designing Kafka Metadata 81

LinkedIn Engineering

DECEMBER 12, 2023

Less stringent privacy baselines have been proposed in the literature, such as event-level privacy which would consider the privacy of each new view in our setting. This metadata is determined as part of an onboarding process with each new application where we determine which algorithms should be used for each type of query.

Algorithm 77

Algorithm Metadata SQL Datasets 77

Ascend.io

NOVEMBER 28, 2023

Data orchestration refers to a wide collection of methods and tools that coordinate any and all types of data-related computing tasks. It zeroes in on the specific tasks required to build, operate, and manage data pipelines. Here lies the crucial difference: data pipeline orchestration is inherently context-aware.

Data Pipeline 52

Data Pipeline IT Data Metadata 52

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Data Engineering Podcast

OCTOBER 14, 2019

Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication.

Data Warehouse 100

Data Warehouse PostgreSQL AWS Programming Language 100

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. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!

Data Collection 100

Data Collection Management High Quality Data Metadata 100

Netflix Tech

MARCH 7, 2024

Data: Fast Data Our main data lake is hosted on S3, organized as Apache Iceberg tables. For ETL and other heavy lifting of data, we mainly rely on Apache Spark. In addition to Spark, we want to support last-mile data processing in Python, addressing use cases such as feature transformations, batch inference, and training.

Systems 90

Systems Media Machine Learning Data Warehouse 90

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

Afterpay Tech

SEPTEMBER 6, 2022

Built-in thread pool with a queue to make event publication truly non-blocking to ensure low latency Although Kafka event publication is mostly asynchronous, there are still synchronous steps, e.g. waiting on metadata update and establishing the first connection with the Kafka cluster.

Kafka 52

Kafka AWS Metadata Data Warehouse 52

Databand.ai

JUNE 28, 2023

Here is a more detailed explanation of how data observability works within the data pipeline: Data ingestion : Observability begins from the point where data is ingested into the pipeline. You can monitor how much data is being ingested, how quickly it’s being processed, and whether there are any errors or delays.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Towards Data Science

APRIL 6, 2023

The Implementation After reading one line or two about the available data processing tools in AWS, I chose to build a data pipeline with Lambda and Glue as data processing components, S3 as storage, and a local Airflow to orchestrate everything. In the process-enem-pdf page , click on add trigger.

AWS 78

AWS Data Pipeline Amazon Web Services Python 78

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. Smart DwH Mover helps in accelerating data warehouse migration.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

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. . All forms of data!

Unstructured Data 81

Unstructured Data Pharmaceutical Data Warehouse MySQL 81

ProjectPro

FEBRUARY 16, 2023

AWS Glue is a fully managed ETL service that automates the process of cataloging, preparing, and cleaning so analysts can focus on analyzing it and not wrangling it. AWS Glue has a central metadata repository called the Glue catalog. The crawler also helps detect any changes in schema and updates metadata as required.

AWS 52

AWS Data Management ETL Tools Management 52

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Cloudera

AUGUST 21, 2020

For example, if a credit card was used in the United States and shortly afterward the same card was used in Spain to withdraw the same amount, these two events in isolation could appear legitimate. However, in the context of time and geography, these two events point to a pattern of fraud.

Banking 59

Banking Kafka Cloud Storage Government 59

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

Data Engineering Weekly

NOVEMBER 6, 2022

link] Pinterest: How Pinterest Leverages Realtime User Actions in Recommendation to Boost Homefeed Engagement Volume Looping in real-time user interaction events with the recommendation engine can significantly improve the user experience. Every data processing engine has one metadata store to integrate.

Data Engineering 40

Data Engineering Data Engineer Engineering Machine Learning 40

Knowledge Hut

OCTOBER 26, 2023

You can use AWS SES (Simple Email Service) to send emails and AWS SNS (Simple Notification Service) to trigger the email-sending process. Once you upload a CSV file to S3, an S3 event is triggered. After the file is imported, the process of mass emailing begins. Amazon Lambda is closely linked to the S3 service in this project.

AWS 52

AWS Coding Project Cloud Computing 52

Cloudera

SEPTEMBER 1, 2020

The CrunchIndexerTool can use Spark to read data from HDFS files into Apache Solr for indexing, and run the data through a so-called morphline for extraction and transformation in an efficient way. Coordinates distribution of data and metadata, also known as shards.

Cloud Storage 66

Cloud Storage Unstructured Data AWS Analytics Application 66

Monte Carlo

APRIL 7, 2021

In my experience, I’ve found that most data problems can be attributed to one or more of these events: An unexpected change in the data feeding into the job, pipeline or system A change in the logic (ETL, SQL, Spark jobs, etc.) A history of data incidents with helpful documentation can help as well.

Data Engineering 40

Data Engineering Data Engineer Engineering Datasets 40

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. This allows for landing immutable blocks of data without delays, in a predictable fashion.

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183

AltexSoft

JULY 18, 2023

It allows data scientists to analyze large datasets and interactively run jobs on them from the R shell. Big data processing. When transformations are applied to RDDs, Spark records the metadata to build up a DAG, which reflects the sequence of computations performed during the execution of the Spark job.

Big Data 52

Big Data Data Process Process Hadoop 52

Knowledge Hut

DECEMBER 21, 2023

A data redundancy layer, a blockchain layer, and a verification and recovery layer make up the mechanism. For availability in the event of server failure, the data redundancy layer replicates the cloud data across multiple cloud servers.

Cloud Computing 52

Cloud Computing Cloud Algorithm Healthcare 52

Confluent

MAY 9, 2019

Event-first thinking enables us to build a new atomic unit: the event. Four pillars of event streaming. Pillar 1 – Business function: Payment processing pipeline. Pillar 4 – Operational plane: Event logging, DLQs and automation. Journey to Event Driven – Part 2: Programming Models for the Event-Driven Architecture.

Kafka 92

Kafka Pipeline-centric Architecture Database-centric 92

Rockset

MARCH 18, 2024

In this blog, we’ll discuss how RAG fits into the paradigm of real-time data processing and show an example product recommendation application using both Kafka and Flink on Confluent Cloud together with Rockset. LLMs like ChatGPT are trained on vast amounts of text data available up to a cutoff date. What is RAG?

Cloud 64

Cloud Building Metadata Kafka 64

ProjectPro

JULY 23, 2021

Real-time Data Processing Application 7. As soon as you upload a CSV file, it will trigger an S3 event. The process of sending the mail to the addresses provided will begin. Upskill yourself for your dream job with industry-level big data projects with source code 4. Windows Virtual Machine – Deployment 3.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

ProjectPro

FEBRUARY 8, 2023

AWS Glue is a widely-used serverless data integration service that uses automated extract, transform, and load ( ETL ) methods to prepare data for analysis. It offers a simple and efficient solution for data processing in organizations. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog.

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

Zalando Engineering

NOVEMBER 29, 2017

For example, the Streams API guarantees fault-tolerant data processing with exactly-once semantics , and it processes data based on event-time i.e., when the data was actually generated in the real world (rather than when it happens to be processed).

Kafka 40

Kafka AWS Amazon Web Services Utilities 40

Confluent

MARCH 20, 2019

You get to focus on the logic of your data processing pipeline. Whether Streams emits every single update or groups updates is irrelevant to the semantics of a data processing application. High-volume applications may not be able to process and transmit every update within the constraints of CPU, memory, network and disk.

Kafka 106

Kafka Programming Process Designing 106

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.

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

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

Confluent

MAY 7, 2019

Although it is the simplest way to subscribe to and access events from Kafka, behind the scenes, Kafka consumers handle tricky distributed systems challenges like data consistency, failover and load balancing. Data processing requirements. We therefore need a way of splitting up the data ingestion work.

Kafka 111

Kafka Accessible Accessibility Metadata 111