Snowflake

APRIL 18, 2024

This blog post explores how Snowflake can help with this challenge. Legacy SIEM cost factors to keep in mind Data ingestion: Traditional SIEMs often impose limits to data ingestion and data retention. Now there are a few ways to ingest data into Snowflake.

Data Lake 78

Data Lake Data Ingestion Bytes Cloud Computing 78

Cloudera

JUNE 28, 2022

In a previous blog of this series, Turning Streams Into Data Products , we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. This blog will be published in two parts.

Process 81

Process Kafka SQL Machine Learning 81

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. Outcome data goes back into the system to train the model.

BI 74

BI Data Ingestion Data Aggregated Data 74

Rockset

JANUARY 30, 2024

With this architecture, users can separate ingestion compute from query compute, all while accessing the same real-time data. Microbatching : An option to microbatch ingestion based on the latency requirements of the use case. 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

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. Big data offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 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

Lyft Engineering

NOVEMBER 29, 2023

In this particular blog post, we explain how Druid has been used at Lyft and what led us to adopt ClickHouse for our sub-second analytic system. Druid at Lyft Apache Druid is an in-memory, columnar, distributed, open-source data store designed for sub-second queries on real-time and historical data.

Kafka 104

Kafka Data Ingestion Datasets Architecture 104

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Datasets 40

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. Read the announcement blog for more details and get started guides.

Retail 72

Retail Healthcare Data Ingestion Consulting 72

ProjectPro

JANUARY 25, 2022

Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things. Finally, you'll find a list of PySpark projects to help you gain hands-on experience and land an ideal job in Data Science or Big Data.

Big Data 52

Big Data Data Process Process Kafka 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. Contents: What is the role of an Azure Data Engineer? Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

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, 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

DECEMBER 14, 2021

So to improve the speed of data analysis, the IRS worked with the combined technology integrating Cloudera Data Platform (CDP) and NVIDIA’s RAPIDS Accelerator for Apache Spark 3.0. However, the CBA is a huge institution with 15 million customers and 700M daily transactions — managing the growing influx of data was challenging. .

Transportation 107

Transportation Telecommunication Banking Data Lake 107

Snowflake

SEPTEMBER 13, 2023

Snowpark External Access – public preview External Access is in public preview on AWS regions. Users can now easily connect to external network locations, including external LLMs, from their Snowpark code while maintaining high security and governance over their data. Snowpark Python Updates Snowpark support for Python 3.9

Python 77

Python SQL Data Pipeline Data Ingestion 77

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 global, cloud-based messaging framework that has become increasingly popular among data engineers over recent years.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

Cloudera

FEBRUARY 16, 2021

In the previous blog post , we looked at some of the application development concepts for the Cloudera Operational Database (COD). In this blog post, we’ll see how you can use other CDP services with COD. Integrated across the Enterprise Data Lifecycle . Cloudera Shared Data Experience (SDX) .

Database 81

Database Machine Learning Data Lake Kafka 81

Monte Carlo

JUNE 22, 2023

They combined these principles with FAIR , a popular framework in the world of pharma that stands for findability, accessibility, interoperability, and reusability. When they started this journey, the data team did not want to leave modeling and architecture behind. So they agreed to leverage Data Vault 2.0

Building 52

Building Data Ingestion Data Business Analyst 52

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

Ascend.io

JUNE 6, 2023

In this blog post, we delve deep into the factors that contribute to how Ascend runs in the cloud, focusing on the primary areas that contribute to costs in this context: storage, compute, networking, and retries. In the large-scale data pipeline networks operated by Ascend, storage can account for 30% of total cloud costs.

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

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

Rockset

MAY 31, 2023

This technical blog highlights the architecture that paved the way for this accomplishment. Understanding real-time workloads High QPS is often crucial for organizations that require real-time or near-real-time processing of a significant volume of queries. This is one reason why p95 query latencies are kept low.

Data Ingestion 52

Data Ingestion Datasets Architecture Retail 52

Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

Machine Learning 98

Machine Learning Manufacturing Data Collection Data Science 98

Cloudera

JULY 9, 2021

If you’ve followed Cloudera for a while, you know we’ve long been singing the praises—or harping on the importance, depending on perspective—of a solid, standalone enterprise data strategy. The ways data strategies are implemented, the resulting outcomes and the lessons learned along the way provide important guardrails.

Government 110

Government Building Cloud Data Ingestion 110

LinkedIn Engineering

DECEMBER 20, 2023

This blog post delves into the AutoML framework for LinkedIn’s content abuse detection platform and its role in improving and fortifying content moderation systems at LinkedIn. Automated Machine Learning (AutoML) refers to a framework or platform that automates the entire machine learning process. What is AutoML?

Machine Learning 90

Machine Learning Datasets Algorithm Architecture 90

Knowledge Hut

MARCH 19, 2024

I will explore the top 10 AWS applications and their use cases in this blog. Security : AWS promotes security and compliance, providing comprehensive security features and controls to protect data and applications. These include encryption, identity and access management , network security, and compliance certifications.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Cloudera

JUNE 7, 2021

CDF streamlines the process of collecting, curating and analyzing real-time streaming data with its integrated set of components. It calls out that Cloudera DataFlow “ includes streaming flow and streaming data processing unified with Cloudera Data Platform ”.

Kafka 101

Kafka Data Ingestion Architecture Cloud 101

Snowflake

AUGUST 16, 2023

Read our Summit recap blog for highlights across industries or watch Summit sessions now on-demand. Applications Snowflake Native App Framework now available in AWS – public preview Snowflake Native Apps are an entirely new way to put data to work. Learn more about ML-Powered Functions in our blog or in Snowflake documentation.

Scala 52

Scala Transportation Kafka Data Lake 52

Data Engineering Weekly

NOVEMBER 20, 2022

[link] The short YouTube video gives a nice overview of the Data Cards. We often think of AI/ ML as a complex data processing problem, but it doesn’t make any use until it is exposed to an end user or an application. The blog narrates one such application that uses video quality with neural networks.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 82

Engineering Raw Data Data Science Scala 82

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

Pinterest Engineering

AUGUST 18, 2022

The HBase Ecosystem, though having various advantages like strong consistency at row level in high volume requests, flexible schema, low latency access to data, Hadoop integration, etc. In this blog post, we will first learn the various approaches considered for data migration with their trade offs.

Data Ingestion 52

Data Ingestion Hadoop Database Kafka 52

Cloudera

NOVEMBER 17, 2020

Once you have deployed the template and all the CML artifacts that go with it, you can unpick and work it backward to map the process to your own data in your own environment. . The data and the techniques presented in this prototype are still applicable as creating a PCA feature store is often part of the machine learning process. .

Deep Learning 110

Deep Learning Machine Learning Raw Data Data Ingestion 110

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

Towards Data Science

JANUARY 26, 2023

In such cases one must consider the manner in which the files will be pulled to the application while taking into account: bandwidth capacity, network latency, and the application’s file access pattern. This continues a series of posts on the topic of efficient ingestion of data from the cloud (e.g., here , here , and here ).

Cloud Storage 75

Cloud Storage Big Data Cloud AWS 75