DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Fast, Safe Development and Deployment (#4) Introduction The integrity and functionality of new code, tools, and configurations during the development and deployment stages are crucial. This process is critical as it ensures data quality from the onset.

Data Ingestion 49

Data Ingestion Datasets Coding Data 49

Snowflake

APRIL 9, 2024

However, that data must be ingested into our Snowflake instance before it can be used to measure engagement or help SDR managers coach their reps — and the existing ingestion process had some pain points when it came to data transformation and API calls. Each of these sources may store data differently.

BI 74

BI Data Ingestion Data Aggregated Data 74

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs.

Raw Data 49

Raw Data Data Ingestion Datasets Data 49

Knowledge Hut

APRIL 23, 2024

Two popular approaches that have emerged in recent years are data warehouse and big data. While both deal with large datasets, but when it comes to data warehouse vs big data, they have different focuses and offer distinct advantages. Data warehousing offers several advantages. Big data offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

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

Rockset

JANUARY 30, 2024

Microbatching : An option to microbatch ingestion based on the latency requirements of the use case. In this blog, we delve into each of these features and how they are giving users more cost controls for their search and AI applications. This is not a hands-free operation and also involves the transfer of data across nodes.

Data Ingestion 64

Data Ingestion Utilities Architecture SQL 64

Cloudera

NOVEMBER 1, 2023

This AMP is built on the foundation of one of our previous AMP s, with the additional enhancement of enabling customers to create a knowledge base from data on their own website using Cloudera DataFlow (CDF) and then augment questions to the chatbot from that same knowledge base in Pinecone.

Machine Learning 105

Machine Learning Data Ingestion Database Architecture 105

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Ensuring Data Quality in New Data Sources (#1) Introduction to Data Evaluation in Data Observability Ensuring their quality and integrity before incorporating new data sources into production is paramount. When looking at new data, does one patch the data?

Data Cleanse 49

Data Cleanse Data Ingestion Data Datasets 49

Christophe Blefari

JANUARY 20, 2024

What is data engineering As I said it before data engineering is still a young discipline with many different definitions. Still, we can have a common ground when mixing software engineering, DevOps principles, Cloud — or on-prem — systems understanding and data literacy. On a regular schedule. This is not.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Rockset

JULY 6, 2022

This is the fifth post in a series by Rockset's CTO and Co-founder Dhruba Borthakur on Designing the Next Generation of Data Systems for Real-Time Analytics. We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! This keeps the data intact.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Data Engineering Weekly

MAY 16, 2023

In the first part of this series, we talked about design patterns for data creation and the pros & cons of each system from the data contract perspective. In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive?

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

Databand.ai

JULY 17, 2023

DataOps , short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Databand.ai

AUGUST 30, 2023

The DataOps framework is a set of practices, processes, and technologies that enables organizations to improve the speed, accuracy, and reliability of their data management and analytics operations. The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed efficiently.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Cloudera

OCTOBER 13, 2021

Production ML systems automate business operations and are capable of handling enormous amounts of real-time data. The book details how to implement such a system, enabling and supporting your business’ growth. . Read the book to find out what they mean, and why NiFi is an essential tool for data ingestion and movement.

Machine Learning 74

Machine Learning Data Ingestion Algorithm Technology 74

Cloudera

DECEMBER 14, 2021

But with growing demands, there’s a more nuanced need for enterprise-scale machine learning solutions and better data management systems. The 2021 Data Impact Awards aim to honor organizations who have shown exemplary work in this area. . For this, the RTA transformed its data ingestion and management processes. .

Transportation 107

Transportation Telecommunication Banking Data Lake 107

Cloudera

AUGUST 11, 2022

And while operations in the cyber-domain are more likely to make the evening news, there are a vast array of critical use cases that support the military’s need for a data architecture that collects, processes, and delivers any type of data, anywhere. . In fact, one of our customers is ingesting data from over 100,000 endpoints.

Transportation 89

Transportation Data Ingestion Architecture Data 89

ProjectPro

FEBRUARY 6, 2023

With over 10 million active subscriptions, 50 million active topics, and a trillion messages processed per day, Google Cloud Pub/Sub makes it easy to build and manage complex event-driven systems. Google Cloud Pub/Sub is a messaging service that allows apps and services to exchange event data. What is Google Pub/Sub?

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. .

Manufacturing 105

Manufacturing Data Warehouse Kafka Retail 105

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

FEBRUARY 8, 2021

As advanced use cases, like advanced driver assistance systems featuring lane change departure detection, advanced vehicle diagnostics, or predictive maintenance move forward, the existing infrastructure of the connected car is being stressed. billion in 2019, and is projected to reach $225.16 billion by 2027, registering a CAGR of 17.1%

Manufacturing 106

Manufacturing Machine Learning Data Ingestion Electronics 106

Cloudera

APRIL 20, 2021

Streaming Analytics is a type of data analysis that processes data streams for real-time analytics. It continuously processes data from multiple streams and performs simple calculations to complex event processing for delivering sophisticated use cases. What is Streaming Analytics?

Hospitality 92

Hospitality Kafka Retail Data Ingestion 92

Cloudera

AUGUST 4, 2021

Using a scalable data management and analytics platform built on Cloudera Enterprise, Sikorsky can process and store data in a reliable way, and analyze full data sets across entire fleets. images, video, text, spectral data) or other input such as thermographic or acoustic signals. .

Unstructured Data 98

Unstructured Data Data Ingestion Government Machine Learning 98

Knowledge Hut

MARCH 19, 2024

I will explore the top 10 AWS applications and their use cases in this blog. Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks. ECS is mostly used in microservices architecture , batch processing, and CI/CD pipelines. What is AWS?

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Snowflake

MARCH 21, 2023

In February, Snowflake launched new features around streaming data ingestion and data governance and improved SQL experience and performance, with enhancements to Search Optimization Service and more. Check out Felipe Hoffa’s video on how to use Snowsight to get from data to decision faster. Learn more here.

Retail 72

Retail Healthcare Data Ingestion Consulting 72

Data Engineering Podcast

JULY 10, 2022

In this episode Chris Riccomini shares his experiences building and scaling data operations at WePay and LinkedIn, as well as the lessons he has learned working with other teams as they automated their own systems. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months.

Data Engineering 100

Data Engineering Data Engineer Engineering MongoDB 100

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

Snowflake

SEPTEMBER 13, 2023

Tabular Return Values from Python Stored Procedures – general availability The general availability of tabular stored procedures with a handler written in Python enables data to be returned in a table format. Learn more about the GEOMETRY data type and ST_TRANSFORM on our blog. Learn more. Learn more. Learn more.

Python 77

Python SQL Data Pipeline Data Ingestion 77

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Pinterest Engineering

AUGUST 18, 2022

It involves data migration from HBase to TiDB, design and implementation of Unified Storage Service, API migration from Ixia/Zen/UMS to Unified Storage Service, and Offline Jobs migration from HBase/Hadoop ecosystem to TiSpark ecosystem while maintaining our availability and latency SLA. This strategy is the simplest and easiest to implement.

Data Ingestion 52

Data Ingestion Hadoop Database Kafka 52

Knowledge Hut

MARCH 22, 2024

It includes the service and capability portfolio that makes the device connectivity, data ingestion, analytics, and integration with other cloud services. In this blog, I will explain all the fundamentals of Azure IoT and how it offers various solutions to cater to businesses worldwide. trillion by 2026.

Cloud Computing 52

Cloud Computing Utilities Cloud Transportation 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. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Python Kafka Java 107

Cloudera

FEBRUARY 9, 2021

Cloudera Operational Database is now available in three different form-factors in Cloudera Data Platform (CDP). . If you are new to Cloudera Operational Database, see this blog post. In this blog post, we’ll look at both Apache HBase and Apache Phoenix concepts relevant to developing applications for Cloudera Operational Database.

Database 101

Database Java Data Ingestion SQL 101

Cloudera

AUGUST 26, 2020

In Part II of our Q&A, Dinesh will be looking at how businesses can leverage technology like Apache Flink and Apache NiFi to promote low latency processing of high-volume, high-velocity data. Hello Dinesh, thank you for joining us for Part II of our Q&A on streaming data. How does that happen in near real-time?

Banking 80

Banking Data Ingestion Kafka Data Lake 80

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

NOVEMBER 17, 2020

Knowing that a transaction is fraudulent is a critical requirement for financial services companies, but knowing that a transaction that was flagged by a rules-based system as fraudulent is a valid transaction, can be equally important. Data analysis – create a plan to build the model. Deploy the model.

Deep Learning 110

Deep Learning Machine Learning Raw Data Data Ingestion 110

Monte Carlo

MARCH 24, 2023

We’ll explore their definitions, purposes, and methods so you can ensure both data integrity and data validity in your organization. What is Data Integrity? Data integrity is the process of maintaining the consistency, accuracy, and trustworthiness of data throughout its lifecycle, including storage, retrieval, and usage.

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52