Snowflake

OCTOBER 16, 2024

The company quickly realized maintaining 10 years’ worth of production data while enabling real-time data ingestion led to an unscalable situation that would have necessitated a data lake. Snowflake's separate clusters for ETL, reporting and data science eliminated resource contention.

Digital Media 81

Digital Media Media Data Lake Data Warehouse 81

Data Engineering Weekly

MAY 16, 2023

Architectural Patterns for Data Quality Now we understand the trade-off between speed & correctness and the difference between data testing and observability. Let’s talk about the data processing types. In the 'Write' stage, we capture the computed data in a log or a staging area.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Edureka

AUGUST 23, 2024

Current and up-to-date data helps enhance the efficiency of services, improve customer experiences, and drive innovation. Data Ingestion Data from different streams, such as applications, sensors, etc., Enabling Data Access Once the data processing is complete, the real-time data is available in the data stream.

AWS 52

AWS Kafka Amazon Web Services Medical 52

Team Data Science

MAY 10, 2020

I can now begin drafting my data ingestion/ streaming pipeline without being overwhelmed. Kafka, while not in the top 5 most in demand skills, was still the most requested buffer technology requested which makes it worthwhile to include it. I'll use Python and Spark because they are the top 2 requested skills in Toronto.

Data Pipeline 130

Data Pipeline Data Ingestion AWS Kafka 130

ProjectPro

JANUARY 25, 2022

Features of PySpark Features that contribute to PySpark's immense popularity in the industry- Real-Time Computations PySpark emphasizes in-memory processing, which allows it to perform real-time computations on huge volumes of data. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency.

Big Data 52

Big Data Data Process Process Kafka 52

Striim

JULY 31, 2024

This flexibility enables organizations to tailor data processing to their specific needs. More Than a Data Streaming Platform Beyond streaming data, Striim delivers on data ingestion through Change Data Capture (CDC), ELT, ETL, and snapshots.

Aggregated Data 52

Aggregated Data Data Ingestion Java 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

Cloudera

JANUARY 20, 2021

The data journey is not linear, but it is an infinite loop data lifecycle – initiating at the edge, weaving through a data platform, and resulting in business imperative insights applied to real business-critical problems that result in new data-led initiatives. STEP 4: Capture data from Apache Kafka streams.

Manufacturing 109

Manufacturing Data Warehouse Kafka Retail 109

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

Data Engineering Weekly

SEPTEMBER 11, 2023

But the key point here is if we can shrink the incremental data processing that can fit into a single machine, the greater the cost efficiency of data infrastructure. We need to take these concepts but should rethink to fit the data model to take advantage of both the software and hardware advancements.

Data Engineering 62

Data Engineering Data Engineer Engineering Kafka 62

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 Engineer 130

Data Engineer Data Engineering Engineering Hadoop 130

Cloudera

JULY 21, 2022

Druid’s native support for ingesting data from Apache Kafka allows it to stream data from Cloudera DataFlow to Rill’s fully managed Druid service. Data is made queryable in real time. The Druid native Kafka indexing service features: Pull-based ingestion. Cloudera Data Warehouse).

BI 86

BI Digital Media Data Warehouse Kafka 86

Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage). Azure SQL Database, Azure Data Lake Storage).

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

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. As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

Knowledge Hut

JUNE 26, 2023

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. Source Code: Stock and Twitter Data Extraction Using Python, Kafka, and Spark 2.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

Striim

JUNE 17, 2024

These roles will span various sectors, including data science, AI ethics, machine learning engineering, and AI-related research and development. Real-Time Data — The Missing Link What is Real-Time Data? Rebuttal: “While real-time systems require an investment, the ROI is substantial.

Algorithm 52

Algorithm Retail Data Ingestion Healthcare 52

Cloudera

JUNE 7, 2021

It calls out that Cloudera DataFlow “ includes streaming flow and streaming data processing unified with Cloudera Data Platform ”.

Kafka 103

Kafka Data Ingestion Cloud Architecture 103

Data Engineering Podcast

OCTOBER 12, 2020

Our comprehensive data-level security, auditing and de-identification features eliminate the need for time-consuming manual processes and our focus on data and compliance team collaboration empowers you to deliver quick and valuable data analytics on the most sensitive data to unlock the full potential of your cloud data platforms.

Business Intelligence 100

Business Intelligence BI Consulting Data Ingestion 100

Knowledge Hut

SEPTEMBER 26, 2023

Organisations are constantly looking for robust and effective platforms to manage and derive value from their data in the constantly changing landscape of data analytics and processing. These platforms provide strong capabilities for data processing, storage, and analytics, enabling companies to fully use their data assets.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Striim

AUGUST 28, 2024

These roles will span various sectors, including data science, AI ethics, machine learning engineering, and AI-related research and development. Real-Time Data — The Missing Link What is Real-Time Data? Misconception: Batch Processing Suffices Objection: Many AI/ML tasks can be handled with batch processing.

Healthcare 52

Healthcare Retail Algorithm Finance 52

Knowledge Hut

APRIL 25, 2023

Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively. Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 52

ProjectPro

DECEMBER 7, 2021

Big data pipelines must be able to recognize and process data in various formats, including structured, unstructured, and semi-structured, due to the variety of big data. Over the years, companies primarily depended on batch processing to gain insights. However, it is not straightforward to create data pipelines.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

DECEMBER 28, 2021

Apache Spark Streaming Use Cases Spark Streaming Architecture: Discretized Streams Spark Streaming Example in Java Spark Streaming vs. Structured Streaming Spark Streaming Structured Streaming What is Kafka Streaming? Kafka Stream vs. Spark Streaming What is Spark streaming? live logs, IoT device data, system telemetry data, etc.)

Architecture 52

Architecture Kafka Java Scala 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

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Despite Hadoop’s parallel and distributed processing, compute was a limited resource as well.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Flat File A type of database that stores data in a plain text format.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

AUGUST 29, 2023

Examples of unstructured data can range from sensor data in the industrial Internet of Things (IoT) applications, videos and audio streams, images, and social media content like tweets or Facebook posts. Data ingestion Data ingestion is the process of importing data into the data lake from various sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Confluent

SEPTEMBER 26, 2019

In the early days, many companies simply used Apache Kafka ® for data ingestion into Hadoop or another data lake. However, Apache Kafka is more than just messaging. Some Kafka and Rockset users have also built real-time e-commerce applications , for example, using Rockset’s Java, Node.js

Kafka 21

Kafka SQL BI Hadoop 21

Knowledge Hut

MAY 2, 2024

As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R. billion (2019 - 2022).

Scala 52

Scala Hospitality Healthcare Machine Learning 52

Knowledge Hut

MARCH 28, 2024

They handle large amounts of structured and unstructured data and use Azure services to develop data processing and analytics pipelines. Role Level: Intermediate Responsibilities Design and develop big data solutions using Azure services like Azure HDInsight, Azure Databricks, and Azure Data Lake Storage.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

Confluent

OCTOBER 10, 2019

A key challenge, however, is integrating devices and machines to process the data in real time and at scale. Apache Kafka ® and its surrounding ecosystem, which includes Kafka Connect, Kafka Streams, and KSQL, have become the technology of choice for integrating and processing these kinds of datasets.

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

AltexSoft

JULY 29, 2022

It relieves the MapReduce engine of scheduling tasks and decouples data processing from resource management. To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

phData: Data Engineering

AUGUST 4, 2023

The following statement lists all the files contained in the internal named stage: List @my_internal_stage; There are several scenarios where internal stages can be used, including: Data Staging: If you need to temporarily store data within Snowflake for processing or analysis, internal stages can be used as a staging area.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Rockset

NOVEMBER 28, 2022

First, CDC theoretically allows companies to analyze and react to data in real time, as it’s generated. It works with existing streaming systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs, making it easier than ever to build a real-time data pipeline. This method offers a few enormous advantages over batch updates.

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB SQL 52