Ascend.io

SEPTEMBER 14, 2023

The rise of data-intensive operations has positioned data engineering at the core of today’s organizations. As the demand to efficiently collect, process, and store data increases, data engineers have started to rely on Python to meet this escalating demand.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Rockset

AUGUST 30, 2021

The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics. That’s because traditional OLTP systems and data warehouses are ill-equipped to power real-time analytics easily or efficiently. You can also optionally use WHERE clauses to filter out data.

SQL 52

SQL Kafka MongoDB MySQL 52

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.

Machine Learning 106

Machine Learning Python Kafka Java 106

DareData

JULY 5, 2023

In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently. To overcome this challenge, many companies are turning to Data Lake solutions, which provide a centralized and scalable platform for storing, processing, and analyzing data.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Knowledge Hut

NOVEMBER 17, 2023

Building, installing, and managing data solutions on the Azure platform will be their responsibility. They will work with other data specialists to ensure that data solutions are successfully integrated into business processes. They must be aware of the development of data systems and how it has affected data specialists.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

DECEMBER 7, 2021

Data Pipeline Tools AWS Data Pipeline Azure Data Pipeline Airflow Data Pipeline Learn to Create a Data Pipeline FAQs on Data Pipeline What is a Data Pipeline? A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

SEPTEMBER 27, 2023

Speed QuickSight's in-memory processing, enhanced by SPICE, allows for quick data retrieval and analysis. This is particularly helpful for large datasets and real-time analytics Power BI optimizes data query performance, nonetheless, the complexity of calculations and data interactions may influence the speed.

BI 52

BI AWS Database-centric Data Lake 52

Rockset

AUGUST 12, 2019

The real-time journey typically starts with live dashboards on real-time data and soon moves to automating actions on that data with applications like instant personalization, gaming leaderboards and smart IoT systems. DynamoDB Streams + Lambda + ElastiCache for Redis 3.

NoSQL 40

NoSQL AWS SQL Datasets 40

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. Order of process phases. What is ELT?

Process 52

Process Building Raw Data Data Lake 52

ProjectPro

JANUARY 25, 2022

PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency. RDD uses a key to partition data into smaller chunks.

Big Data 52

Big Data Data Process Process Kafka 52

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. ClickHouse has several storage engines that can pre-aggregate data.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

LinkedIn Engineering

DECEMBER 20, 2023

We also outline the complex systems that underpin our anti-abuse efforts, discussing the challenges and solutions we have designed along the way. Let’s look into the critical modules that are needed to build this type of system. We need systems to ingest, process, and analyze the data efficiently.

Building 91

Building Algorithm Kafka Machine Learning 91

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. However, because most institutions lack a modern data architecture , they struggle to manage, integrate and analyze financial data at pace.

Data Architecture 82

Data Architecture Architecture Management Banking 82

Picnic Engineering

OCTOBER 31, 2023

To ensure this availability we need to be able to see what our systems are doing at any point making the observability of our systems essential. Datadog aggregates data based on the specific “operations” they are associated with, such as acting as a server, client, RabbitMQ interaction, database query, or various methods.

Java 52

Java Aggregated Data Coding Python 52

Lyft Engineering

SEPTEMBER 27, 2022

The team needed better infrastructure to make the dynamic pricing system more reactive for the following reasons: Decrease end-to-end latency that would make the system more reactive to marketplace imbalances. This pipeline ingests tens of millions of events per second and processes them into machine learning features.

Kafka 52

Kafka Aggregated Data Machine Learning Architecture 52

ProjectPro

JUNE 28, 2016

Apache Kafka is breaking barriers and eliminating the slow batch processing method that is used by Hadoop. To explain Apache Kafka in a simple manner would be to compare it to a central nervous system than collects data from various sources. This data is constantly changing, and is voluminous.

Kafka 40

Kafka Systems Hadoop BI 40

ProjectPro

FEBRUARY 8, 2023

In 2023, more than 5140 businesses worldwide have started using AWS Glue as a big data tool. For e.g., Finaccel, a leading tech company in Indonesia, leverages AWS Glue to easily load, process, and transform their enterprise data for further processing. AWS Glue automates several processes as well.

AWS 98

AWS Scala Metadata Data Lake 98

AltexSoft

OCTOBER 21, 2022

Similar to Google in web browsing and Photoshop in image processing, it became a gold standard in data streaming, preferred by 70 percent of Fortune 500 companies. Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Rockset

FEBRUARY 16, 2024

As a payment system that operates by taking a percentage of the transaction fee from the merchant, the reliability of payment integration with the merchant and other partners' systems is of utmost importance. The enriched data is streamed to Rockset where it is pre-aggregated and indexed for serving alerts and monitoring dashboards.

Architecture 59

Architecture SQL Data Warehouse Database 59

Databand.ai

JULY 10, 2023

Observability platforms gather, examine, and display telemetry data from various sources like logs, metrics, and trace data. By offering a comprehensive view of system performance and user experience, these platforms enable teams to proactively identify issues and enhance application performance.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Aggregated Data 52

Netflix Tech

JULY 12, 2019

delivering a large amount of business value in the process. Gatekeeper is the system at Netflix responsible for evaluating the “liveness” of videos and assets on the site. and can achieve orders of magnitude more efficient data access, which opens up many possibilities. The team responsible for this curation is Title Operations.

Datasets 73

Datasets Kafka Architecture Aggregated Data 73

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. In the event that they are not the same, what are the difference s? To provide meaningful business insights, it collects and manages data from a variety of sources. Data Warehouse in DBMS: .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Rockset

NOVEMBER 1, 2022

Step 1: Data Acquisition Elasticsearch is rarely the system of record which means the data in it comes from somewhere else for real-time analytics. Rockset has built-in connectors to stream real-time data for testing and simulating production workloads including Apache Kafka , Kinesis and Event Hubs.

Database-centric 52

Database-centric Pipeline-centric SQL Aggregated Data 52

phData: Data Engineering

JANUARY 3, 2022

When it comes to adding value to data, there are many things you have to take into account — both inside and outside your company. The best option will vary depending on whether your data is structured or unstructured (or even semi-structured), normalized or denormalized, and whether you need data in a row or columnar data format.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Scott Logic

JULY 26, 2023

The theory was that the bot would be able to interact with the bank’s systems and with the user so as to enrich the bank’s understanding of the customer and connect them to the most suitable products and services. Not all of the data needs to be sought from the customer. The Knowledge Graph can pull data from other systems.

Banking 52

Banking Aggregated Data Retail Architecture 52

AltexSoft

SEPTEMBER 21, 2023

From those home-made beginnings as Compass, Elasticsearch has matured into one of the leading enterprise search engines, standing among the top 10 most popular database management systems globally according to the Stack Overflow 2023 Developer Survey. Fluentd is a data collector and a lighter-weight alternative to Logstash.

Engineering 52

Engineering NoSQL Programming Language Java 52

Rockset

MARCH 1, 2023

As a result, compute contention ensues, causing several problems for customers and prospects: User-facing analytics in my SaaS application can only update every 30 minutes since the underlying database becomes unstable whenever I try to process streaming data continuously.

Architecture 59

Architecture AWS SQL Cloud Storage 59

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 Engineering 52

Data Engineering Data Engineer Coding Project 52

Picnic Engineering

SEPTEMBER 8, 2022

Almost all internal services emit events over RabbitMQ. Our pipeline captures these events and sends them to Confluent Cloud. Now we are going to take a deeper look into each sub-part of our system. RabbitMQ We have already mentioned that RabbitMQ is used as the main inter-service communication event bus at Picnic.

Kafka 52

Kafka Metadata AWS Java 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Confluent

MARCH 28, 2019

At Confluent, we want to help developers understand how to think about event streaming and the opportunities it can create. Educating people on what an event stream looks like is a daunting task. Traditionally, making sense of the data flowing in a distributed event streaming platform is done by charts and graphs of aggregated data.

Kafka 74

Kafka Aggregated Data Media Engineering 74

Confluent

MARCH 28, 2019

At Confluent, we want to help developers understand how to think about event streaming and the opportunities it can create. Educating people on what an event stream looks like is a daunting task. Traditionally, making sense of the data flowing in a distributed event streaming platform is done by charts and graphs of aggregated data.

Kafka 77

Kafka Aggregated Data Media Engineering 77

Rockset

FEBRUARY 25, 2021

It’s difficult to create data analytics systems that can easily query across your various data sources while maintaining fast performance and real-time capabilities. Elasticsearch , originally developed for text search, has recently tried to push into the data analytics space. This can be a challenge, though.

SQL 40

SQL Data Pipeline Kafka Database 40

ProjectPro

NOVEMBER 15, 2021

As per the surveyors, Big data (35 percent), Cloud computing (39 percent), operating systems (33 percent), and the Internet of Things (31 percent) are all expected to be impacted by open source shortly. Following these statistics, big data is set to get bigger with the evolution of open-source projects.

Big Data 52

Big Data Project Metadata Programming Language 52

Cloudera

NOVEMBER 13, 2020

Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? It turns out that Apache Impala scales down with data just as well as it scales up. This is part of our series of blog posts on recent enhancements to Impala. The entire collection is available here.

Metadata 142

Metadata Coding SQL Database 142

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

DoorDash Engineering

APRIL 12, 2023

A centralized metrics layer ensures accurate and reliable measurement of experiment results and streamlines the analysis process by minimizing the need for ad-hoc analysis. We have our in-house experimentation analysis platform called Curie , which automates and unifies the process of analyzing experiments at DoorDash.

SQL 82

SQL Metadata Raw Data Government 82