Snowflake

FEBRUARY 13, 2025

Lastly, companies have historically collaborated using inefficient and legacy technologies requiring file retrieval from FTP servers, API scraping and complex data pipelines. These processes were costly and time-consuming and also introduced governance and security risks, as once data is moved, customers lose all control.

Management 92

Management Government Cloud Unstructured Data 92

Data Engineering Weekly

JUNE 24, 2024

Experience Enterprise-Grade Apache Airflow Astro augments Airflow with enterprise-grade features to enhance productivity, meet scalability and availability demands across your data pipelines, and more. A few highlights from the report Unstructured data goes mainstream. AI-driven code development is going mainstream now.

Data Engineering 109

Data Engineering Data Engineer Engineering Google Cloud 109

Snowflake

JULY 10, 2023

Previously, working with these large and complex files would require a unique set of tools, creating data silos. Now, with unstructured data processing natively supported in Snowflake, we can process netCDF file types, thereby unifying our data pipeline. ” U.S.

Unstructured Data 40

Unstructured Data Python Process Scala 40

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

Data Engineering Weekly

SEPTEMBER 18, 2024

The Rise of Data Observability Data observability has become increasingly critical as companies seek greater visibility into their data processes. This growing demand has found a natural synergy with the rise of the data lake. However, as with any advanced tool, data observability comes with costs and complexities.

Data Lake 98

Data Lake Data Pipeline Unstructured Data Data 98

Monte Carlo

FEBRUARY 12, 2024

Today, this first-party data mostly lives in two types of data repositories. If it is structured data then it’s often stored in a table within a modern database, data warehouse or lakehouse. If it’s unstructured data, then it’s often stored as a vector in a namespace within a vector database.

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Snowflake

APRIL 8, 2024

Many entries also used Snowpark , taking advantage of the ability to work in the code they prefer to develop data pipelines, ML models and apps, then execute in Snowflake. BigGeo BigGeo accelerates geospatial data processing by optimizing performance and eliminating challenges typically associated with big data.

Pipeline-centric 131

Pipeline-centric Food Healthcare Unstructured Data 131

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively. Reflow — A system for incremental data processing in the cloud.

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

Snowflake

SEPTEMBER 7, 2023

Sherif Nada, Founding Member & Engineering Manager, Airbyte “External Access in Snowpark is one of the most awaited features for our internal data engineering team at Snowflake. Snowpark External Access is leveraged to build a Ingest and Reverse ETL data pipeline for production workload.

Amazon Web Services 134

Amazon Web Services AWS Government Python 134

Towards Data Science

DECEMBER 4, 2023

We’ll build a data architecture to support our racing team starting from the three canonical layers : Data Lake, Data Warehouse, and Data Mart. Data Lake A data lake would serve as a repository for raw and unstructured data generated from various sources within the Formula 1 ecosystem: telemetry data from the cars (e.g.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Lake 98

Knowledge Hut

JUNE 26, 2023

From exploratory data analysis (EDA) and data cleansing to data modeling and visualization, the greatest data engineering projects demonstrate the whole data process from start to finish. Data pipeline best practices should be shown in these initiatives. Source Code: Yelp Review Analysis 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Cloudera

NOVEMBER 22, 2021

Open source frameworks such as Apache Impala, Apache Hive and Apache Spark offer a highly scalable programming model that is capable of processing massive volumes of structured and unstructured data by means of parallel execution on a large number of commodity computing nodes. .

Hadoop 94

Hadoop Government Data Security Cloud 94

Data Engineering Weekly

DECEMBER 16, 2024

Vector Search and Unstructured Data Processing Advancements in Search Architecture In 2024, organizations redefined search technology by adopting hybrid architectures that combine traditional keyword-based methods with advanced vector-based approaches.

Data Engineering 62

Data Engineering Data Engineer Engineering Unstructured Data 62

Cloudera

AUGUST 16, 2021

Hundreds of built-in processors make it easy to connect to any application and transform data structures or data formats as needed. Since it supports both structured and unstructured data for streaming and batch integrations, Apache NiFi is quickly becoming a core component of modern data pipelines.

Cloud 122

Cloud Unstructured Data Utilities Metadata 122

Towards Data Science

MAY 3, 2023

VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. You can use VDK to build data lakes and ingest raw data extracted from different sources, including structured, semi-structured, and unstructured data.

Data Lake 98

Data Lake SQL Data Data Warehouse 98

Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. What is the role of a Data Engineer? They are also accountable for communicating data trends. These are as follows: 1.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

Sync Computing

JANUARY 27, 2025

Clusters in Databricks Databricks offers Job clusters for data pipeline processing and warehouse clusters used for the SQL lakehouse. Amazon S3 : Highly scalable, durable object storage designed for storing backups, data lakes, logs, and static content. Job clusters have far more sizing options. R7g , X2idn ) are ideal.

AWS 52

AWS Data Lake Machine Learning Big Data 52

Cloudera

FEBRUARY 15, 2024

Scalability and future-proofing: Modern data architecture offers robust data integration capabilities, allowing efficient and real-time data ingestion from various sources, including structured databases, unstructured data, streaming data, and external data feeds.

Insurance 88

Insurance Banking Data Architecture Data Ingestion 88

Cloudera

OCTOBER 7, 2021

A data mesh can be defined as a collection of “nodes”, typically referred to as Data Products, each of which can be uniquely identified using four key descriptive properties: . Data and Metadata: Data inputs and data outputs produced based on the application logic.

Architecture 124

Architecture Metadata Kafka Government 124

Knowledge Hut

MAY 3, 2024

Cluster Computing: Efficient processing of data on Set of computers (Refer commodity hardware here) or distributed systems. It’s also called a Parallel Data processing Engine in a few definitions. Spark is utilized for Big data analytics and related processing. Happy Learning!!!

Hadoop 98

Hadoop Scala Healthcare Big Data 98

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Unstructured Data 52

Unstructured Data SQL Data Pipeline Data Validation 52

Monte Carlo

OCTOBER 10, 2024

Let’s dive into the responsibilities, skills, challenges, and potential career paths for an AI Data Quality Analyst today. Table of Contents What Does an AI Data Quality Analyst Do? Collaboration Tools : GitHub, JIRA Of course, additional data engineering and data analytics skills are useful in this role as well.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Meltano

OCTOBER 5, 2022

The goal of DataOps is to speed up the process of deriving value from data. For this purpose, various parts of the data pipeline are automated to deliver analytics quickly and efficiently. This results in a system that gives organizations control over the data flow so that anomalies can be spotted automatically.

Engineering 52

Engineering Raw Data Data Pipeline Data Warehouse 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. What is ELT (Extract, Load, Transform)? ELT vs. ETL: What Is the Difference?

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Databand.ai

JULY 19, 2023

This capability is particularly useful in complex data landscapes, where data may pass through multiple systems and transformations before reaching its final destination Impact analysis: When changes are made to data sources or data processing systems, it’s critical to understand the potential impact on downstream processes and reports.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

Knowledge Hut

SEPTEMBER 26, 2023

A person who designs and implements data management , monitoring, security, and privacy utilizing the entire suite of Azure data services to meet an organization's business needs is known as an Azure Data Engineer. The main exam for the Azure data engineer path is DP 203 learning path.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Data Visualization Graphic representation of a set or sets of data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Snowflake

JUNE 27, 2023

Snowflake users can now also replicate Streams and Tasks in GA — these are often used together to build modern data pipelines. We have thousands of Snowflake customers developing powerful data transformation pipelines every single day. Based on internal Snowflake data from August 25, 2022 to April 30, 2023.

Data Governance 59

Data Governance Unstructured Data Government SQL 59

ProjectPro

JANUARY 24, 2023

AWS Glue is a fully managed extract, transform, and load (ETL) service that simplifies the preparation and loading of data for analytics. AWS Glue provides the functionality required by enterprises to build ETL pipelines. The user only needs to define a data pipeline and the processes they want to perform when data flows through it.

AWS 52

AWS Cloud Amazon Web Services ETL Tools 52

Striim

AUGUST 31, 2023

Showing how Kappa unifies batch and streaming pipelines The development of Kappa architecture has revolutionized data processing by allowing users to quickly and cost-effectively reduce data integration costs. Finally, kappa architectures are not suitable for all types of data processing tasks.

Data Integration 69

Data Integration Architecture Amazon Web Services Machine Learning 69

Monte Carlo

AUGUST 25, 2023

With pre-built functionalities and robust SQL support, data warehouses are tailor-made to enable swift, actionable querying for data analytics teams working primarily with structured data. Data lakes also typically decouple storage and comput e, which can enable cost savings while facilitating real-time streaming and querying.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. Let’s take a closer look.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

Knowledge Hut

DECEMBER 22, 2023

Statistics are used by data scientists to collect, assess, analyze, and derive conclusions from data, as well as to apply quantifiable mathematical models to relevant variables. Microsoft Excel An effective Excel spreadsheet will arrange unstructured data into a legible format, making it simpler to glean insights that can be used.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

DareData

JULY 5, 2023

We've seen this happen in dozens of our customers: data lakes serve as catalysts that empower analytical capabilities. If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. And what is the reason for that?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? Also, they must have in-depth knowledge of data processing languages like Python, Scala, or SQL.

Data Architect 87

Data Architect Certification Generalist Big Data 87

Monte Carlo

FEBRUARY 25, 2025

Data pipelines are messy. Data engineering design patterns are repeatable solutions that help you structure, optimize, and scale data processing, storage, and movement. They make data workflows more resilient and easier to manage when things inevitably go sideways. Thats why solid design patterns matter.

Designing 52

Designing Data Engineering Data Engineer Engineering 52