Lyft Engineering

NOVEMBER 29, 2023

Introduction At Lyft, we have used systems like Apache ClickHouse and Apache Druid for near real-time and sub-second analytics. Sub-second query systems allow for near real-time data explorations and low latency, high throughput queries, which are particularly well-suited for handling time-series data.

Kafka 104

Kafka Data Ingestion Datasets Architecture 104

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. Use cases include: Enable transparent access to financial data. Possible applications include: Improved customer risk profiling.

Data Architecture 84

Data Architecture Architecture Management Banking 84

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

Striim

OCTOBER 11, 2024

Change Data Capture (CDC) plays a key role here by capturing and streaming only the changes (inserts, updates, deletes) in real time, ensuring efficient data handling and up-to-date information across systems. Why are Data Pipelines Significant? Now that we’ve answered the question, ‘What is a data pipeline?’

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

LinkedIn Engineering

DECEMBER 19, 2023

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. During cluster degradations, the framework auto-pauses and resumes, mitigating potential intricacies.

Big Data 92

Big Data Hadoop Metadata Data 92

ProjectPro

JUNE 28, 2016

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. This data can be anything from clickstream data, activity/ web logs, consumer data, etc.

Kafka 40

Kafka Systems Hadoop Big Data 40

Striim

JULY 10, 2024

Data Collection and Integration: Data is gathered from various sources, including sensor and IoT data, transportation management systems, transactional systems, and external data sources such as economic indicators or traffic data. Here’s the process. The next phase is model development.

Management 52

Management Transportation Machine Learning High Quality Data 52

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. What does the high-performance data project have to do with the real Franz Kafka’s heritage? process data in real time and run streaming analytics. Practically, nothing. Kafka cluster and brokers.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

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

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

Rockset

AUGUST 25, 2021

It’s simply too expensive to store all the raw data and simply too slow to run batch processes to pre-aggregate it. One common example is a mobile app, where every activity is recorded as an event, resulting in millions of events per day streaming in.

Aggregated Data 52

Aggregated Data Hadoop SQL Data Lake 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

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

Knowledge Hut

JANUARY 19, 2024

New Analytics Strategy vs. Existing Analytics Strategy Business Intelligence is concerned with aggregated data collected from various sources (like databases) and analyzed for insights about a business' performance. Ease of Operations BI systems make it easy for businesses to store, access and analyze data.

Business Intelligence 52

Business Intelligence BI Business Analyst Aggregated Data 52

Knowledge Hut

JANUARY 3, 2024

A SOC Analyst and the SOC team of a company protect the sensitive data and information of a company that is stored in a computer device so that hackers do not get their hands on it and uses this information for malicious activities and purposes. This involves both the implementation of new systems and the updating of current ones as needed.

Computer Science 52

Computer Science Certification Data Mining Data Security 52

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 Data Engineering 52

Netflix Tech

JULY 12, 2019

Gatekeeper is the system at Netflix responsible for evaluating the “liveness” of videos and assets on the site. Gatekeeper accomplishes its prescribed task by aggregating data from multiple upstream systems, applying some business logic, then producing an output detailing the status of each video in each country.

Datasets 73

Datasets Kafka Architecture Computer Science 73

ProjectPro

FEBRUARY 8, 2023

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Create schedules or events that will act as job triggers.

AWS 98

AWS Scala Metadata Data Lake 98

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 Engineering Media 74

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? What is Data Warehouse? . Built to make strategic use of data, a Data Warehouse is a combination of technologies and components.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 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

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

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 79

Kafka Aggregated Data Engineering Media 79

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

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. You need to think about the whole model lifecycle.

Machine Learning 107

Machine Learning Python Kafka Java 107

Ascend.io

SEPTEMBER 14, 2023

In summary, Python’s combination of simplicity, power, and extensive support makes it a compelling choice for data engineering. Whether an engineer is starting on a fresh project or integrating into existing systems, Python provides the tools and community to ensure success. csv') data_excel = pd.read_excel('data2.xlsx')

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

AltexSoft

DECEMBER 23, 2022

ELT is now gaining popularity as an alternative to a traditional ETL (Extract, Transform, Load) process, in which the transformation phase occurs before the data is loaded into a target system. One of the main reasons behind this is the need to timely process huge volumes of data in any format. ELT vs ETL. Scalability.

Process 52

Process Building Raw Data Data Lake 52

Rockset

MARCH 1, 2023

Furthermore, shared real-time data reduces the cost of hot storage significantly, as only one copy of the data is required. In other systems that use replicas for concurrency scaling, each replica needs to individually process the incoming data from the stream which is compute-intensive.

Architecture 59

Architecture AWS SQL Cloud Storage 59

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. Analysis of logs, metrics, and security events. Real-time behavior modeling with ML.

Engineering 52

Engineering NoSQL Programming Language Java 52

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 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? An ETL pipeline is a series of procedures that comprises extracting and transforming data from a data source.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JANUARY 25, 2022

When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. RDDs are also fault-tolerant; thus, they will automatically recover in the event of a failure.

Big Data 52

Big Data Data Process Process Kafka 52

Knowledge Hut

NOVEMBER 17, 2023

Candidates who want to work as Azure data engineers should be familiar with the changing data landscape. They must be aware of the development of data systems and how it has affected data specialists. The distinctions between on-premises and cloud data solutions should be understood by candidates.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

SEPTEMBER 27, 2023

Example: Imagine that your team is analyzing sales data for an internet consumer company with millions of transactions that happen weekly. QuickSight's SPICE engine stores the aggregated data in memory, allowing very fast query response times.

BI 52

BI AWS Database-centric Data Lake 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. For example, an enterprise might be using Amazon Web Services (AWS) as a cloud provider, and you want to store and query data from various systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

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

OCTOBER 28, 2015

Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data. The complexity of the big data system increases with each data source.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52