AltexSoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Snowflake

APRIL 18, 2024

Legacy SIEM cost factors to keep in mind Data ingestion: Traditional SIEMs often impose limits to data ingestion and data retention. Snowflake allows security teams to store all their data in a single platform and maintain it all in a readily accessible state, with virtually unlimited cloud data storage capacity.

Data Lake 80

Data Lake Data Ingestion Bytes Cloud Computing 80

Cloudera

NOVEMBER 1, 2023

And so we are thrilled to introduce our latest applied ML prototype (AMP) — a large language model (LLM) chatbot customized with website data using Meta’s Llama2 LLM and Pinecone’s vector database. High-level overview of real-time data ingest with Cloudera DataFlow to Pinecone vector database.

Machine Learning 108

Machine Learning Data Ingestion Database Architecture 108

Rockset

DECEMBER 19, 2023

From his early days at Quora to leading projects at Facebook and his current venture at Fennel (a real-time feature store for ML), Nikhil has traversed the evolving landscape of machine learning engineering and machine learning infrastructure specifically in the context of recommendation systems.

Systems 52

Systems Machine Learning Deep Learning Media 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

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.

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Knowledge Hut

APRIL 23, 2024

For example, in 1880, the US Census Bureau needed to handle the 1880 Census data. They realized that compiling this data and converting it into information would take over 10 years without an efficient system. Thus, it is no wonder that the origin of big data is a topic many big data professionals like to explore.

Big Data 52

Big Data Amazon Web Services Media Cloud Computing 52

Knowledge Hut

SEPTEMBER 26, 2023

This is particularly valuable in today's data landscape, where information comes in various shapes and sizes. Effective Data Storage: Azure Synapse offers robust data storage solutions that cater to the needs of modern data-driven organizations. Key Features of Databricks 1.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Knowledge Hut

APRIL 23, 2024

It is designed to support business intelligence (BI) and reporting activities, providing a consolidated and consistent view of enterprise data. Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Snowflake

NOVEMBER 29, 2023

For example, the data storage systems and processing pipelines that capture information from genomic sequencing instruments are very different from those that capture the clinical characteristics of a patient from a site. A conceptual architecture illustrating this is shown in Figure 3.

Metadata 84

Metadata Healthcare Medical Data Storage 84

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 Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

MARCH 28, 2024

Job Role 1: Azure Data Engineer Azure Data Engineers develop, deploy, and manage data solutions with Microsoft Azure data services. They use many data storage, computation, and analytics technologies to develop scalable and robust data pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Towards Data Science

DECEMBER 1, 2023

Technical Overview The project architecture is depicted as follows: Google Calendar -> Fivetran -> Snowflake -> dbt -> Snowflake Dashboard , with GitHub Actions orchestrating the deployment. Storage —  Snowflake Snowflake, a cloud-based data warehouse tailored for analytical needs, will serve as our data storage solution.

Data Engineering 71

Data Engineering Data Engineer Project Engineering 71

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

Knowledge Hut

NOVEMBER 2, 2023

They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems. They work on creating efficient data pipelines, optimizing data processing, and ensuring data availability for reporting and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Cloudera

AUGUST 9, 2022

As customers import their mainframe and legacy data warehouse workloads, there is an expectation on the platform that it can meet, if not exceed, the resilience of the prior system and its associated dependencies. The goal is to minimize the impact to a customer’s data-driven decision making in the time of an operational crisis.

Data Lake 77

Data Lake Data Warehouse Architecture Data Ingestion 77

Ascend.io

JANUARY 2, 2024

These are the interfaces where the pipeline taps into various systems to acquire data. The sources of data can be incredibly diverse, ranging from data warehouses, relational databases, and web analytics to CRM platforms, social media tools, and IoT device sensors.

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 52

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 108

Manufacturing Machine Learning Data Ingestion Electronics 108

Knowledge Hut

JANUARY 18, 2024

Artificial Intelligence, at its core, is a branch of Computer Science that aims to replicate or simulate human intelligence in machines and systems. These streams basically consist of algorithms that seek to make either predictions or classifications by creating expert systems that are based on the input data.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional who is in charge of designing, implementing, and maintaining data processing systems and solutions on the Microsoft Azure cloud platform. A Data Engineer is responsible for designing the entire architecture of the data flow while taking the needs of the business into account.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

MAY 12, 2023

Generated by various systems or applications, log files usually contain unstructured text data that can provide insights into system performance, security, and user behavior. Sensor data. A fixed schema means the structure and organization of the data are predetermined and consistent.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Data Engineering Weekly

NOVEMBER 13, 2022

[link] Meta: Tulip - Schematizing Meta’s data platform Numerous heterogeneous services make up a data platform, such as warehouse data storage and various real-time systems. The schematization of data plays a vital role in a data platform.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 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. Key components of the Elasticsearch architecture.

Engineering 52

Engineering NoSQL Programming Language Java 52

Knowledge Hut

OCTOBER 3, 2023

The task of integrating, manipulating, and merging data from diverse structured and unstructured sources into a structure utilized to build analytics solutions falls within the purview of an Azure Data Engineer, a highly qualified specialist. As a result, they can work on a number of projects and use cases.

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. In Snowflake, there are three different storage layers available, Database, Stage, and Cloud Storage.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Knowledge Hut

JANUARY 3, 2024

They work closely with stakeholders to understand business requirements and translate them into scalable, secure, cost-effective Azure architectures. 1-3 years of experience in IT administration or system management. Experience in data modeling, data ingestion, and data transformation.

Cloud Computing 52

Cloud Computing Recruitment Computer Science Education 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

MARCH 14, 2023

Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? A data stack, in turn, focuses on data : It helps businesses manage data and make the most out of it. Modern data stack architecture.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Data-first companies have embraced data platforms as an effective way to aggregate, operationalize, and democratize data at scale across the organization. Data Storage and Processing The first layer? Image courtesy of Monte Carlo.

Building 52

Building BI Data Lake Data Governance 52

Monte Carlo

MARCH 28, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Knowledge Hut

APRIL 25, 2023

Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale. Data engineering tools can help data engineers streamline many of these tasks, allowing them to be more productive and effective in their work.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Towards Data Science

APRIL 3, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Silectis

JANUARY 3, 2021

” Artificial Intelligence AI is a broad term used to describe engineered systems that have been taught to do a task that typically requires human intelligence. BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

FEBRUARY 25, 2022

Efficient Scheduling and Runtime Increased Adaptability and Scope Faster Analysis and Real-Time Prediction Introduction to the Machine Learning Pipeline Architecture How to Build an End-to-End a Machine Learning Pipeline? The final sample used for training and testing the model is the output of data preprocessing.

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52