Snowflake

JUNE 6, 2024

Schedule data ingestion, processing, model training and insight generation to enhance efficiency and consistency in your data processes. That’s why we partner with Hex , to provide data teams with access to best-in-class tools to drive more decisions for the business and more value from their data.

SQL 96

SQL Python Machine Learning Data Workflow 96

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. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Ascend.io

DECEMBER 11, 2023

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. It automates and optimizes data processes, reducing manual effort and the likelihood of errors. But let’s step back for a second.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

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.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Podcast

AUGUST 28, 2022

What are the different concerns that need to be included in a stack that supports fully automated data workflows? There was recently an interesting article suggesting that the "left-to-right" approach to data workflows is backwards.

Data Engineer 100

Data Engineer Data Engineering MongoDB Metadata 100

Ascend.io

OCTOBER 28, 2024

Advanced Data Transformation Techniques For data engineers ready to push the boundaries, advanced data transformation techniques offer the tools to tackle complex data challenges and drive innovation.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Netflix Tech

NOVEMBER 14, 2023

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. Stay tuned for a new post on this!

Metadata 99

Metadata Data Pipeline Scala Data Workflow 99

Data Engineering Podcast

JANUARY 30, 2022

Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. What are some of the utility features that you have found most helpful for data processing? Pandas is a tool that spans data processing and data science.

Data Engineer 100

Data Engineer Data Engineering Engineering Python 100

Cloudyard

JUNE 11, 2024

Read Time: 1 Minute, 48 Second RETRY LAST: In modern data workflows, tasks are often interdependent, forming complex task chains. Ensuring the reliability and resilience of these workflows is critical, especially when dealing with production data pipelines.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Workflow 52

Data Engineering Weekly

SEPTEMBER 29, 2024

The article details how bypassing intermediate storage steps reduces latency and improves data processing speed. The approach highlights the importance of streamlining data workflows for faster machine learning model training and deployment.

Data Engineer 62

Data Engineer Data Engineering Engineering SQL 62

Monte Carlo

JULY 9, 2024

Overcoming Challenges to Achieve Data Timeliness Despite the clear benefits of timely data, organizations often encounter several hurdles: Complex Data Pipelines: Intricate data pipelines that involve multiple stages and dependencies can hinder smooth data flow.

Food 52

Food Data Pipeline Data Data Workflow 52

Data Engineering Weekly

DECEMBER 25, 2023

These technologies are increasingly automating processes like ETL, improving data quality management, and evolving the landscape of data tools. Integrating AI into data workflows is not just a trend but a paradigm shift, making data processes more efficient and intelligent.

Data Engineer 52

Data Engineer Data Engineering Engineering Unstructured Data 52

Ascend.io

DECEMBER 19, 2023

Snowflake’s Data Marketplace : Enriches data pipelines with external data sources, providing access to a diverse range of datasets and services that can be seamlessly integrated into your analytics and data processing workflows. that you can combine to create custom data workflows.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Ascend.io

JULY 11, 2024

It enhances data quality, governance, and optimization, making data retrieval more efficient and enabling powerful automation in data engineering processes. As practitioners using metadata to fuel data teams, we at Ascend understand the critical role it plays in organizing, managing, and optimizing data workflows.

Metadata 52

Metadata IT Government High Quality Data 52

Striim

SEPTEMBER 11, 2024

Furthermore, Striim also supports real-time data replication and real-time analytics, which are both crucial for your organization to maintain up-to-date insights. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis. Are we using all the data or just a subset?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Ascend.io

JUNE 10, 2024

At Ascend, we recognize the immense potential in integrating these advanced AI capabilities into our platform, enabling smarter applications and more efficient data workflows. Snowflake’s investment in expanding data engineering capabilities is a game-changer.

Data Engineer 52

Data Engineer Data Engineering Engineering High Quality Data 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Lake 52

Meltano

OCTOBER 5, 2022

In the same way, a DataOps engineer designs the data assembly line that enables data scientists to derive insights from data analytics faster and with fewer errors. DataOps engineers improve the speed and quality of the data development process by applying DevOps principles to data workflow, known as DataOps.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

Monte Carlo

FEBRUARY 8, 2024

Table of Contents Data Pipeline Optimization: Cost Data Pipeline Optimization: Processing Speed Data Pipeline Optimization: Resilience Data Pipeline Optimization: Data Quality Data Pipeline Optimization: Cost The goal here is to balance robust data processing capabilities with cost control.

Data Pipeline 52

Data Pipeline AWS Datasets Data 52

Monte Carlo

APRIL 28, 2023

DuckDB’s parallel execution capabilities can help DBAs improve the performance of data processing tasks. Researchers : Academics and researchers working with large volumes of data use DuckDB to process and analyze their data more efficiently. What makes DuckDB different?

Data Warehouse 52

Data Warehouse Programming Language Datasets Database 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 50

Raw Data Data Ingestion Datasets Data 50

Monte Carlo

NOVEMBER 14, 2023

But even as the modern data stack continues to evolve, Airflow maintains its title as a perennial data orchestration favorite—and for good reason. Prefect’s control panel also offers scheduling, automatic retries, and instant alerting, ensuring you always have a clear view of your data processes.

Pipeline-centric 40

Pipeline-centric Google Cloud Python Data Workflow 40

Knowledge Hut

NOVEMBER 2, 2023

These Azure data engineer projects provide a wonderful opportunity to enhance your data engineering skills, whether you are a beginner, an intermediate-level engineer, or an advanced practitioner. Who is Azure Data Engineer? Azure SQL Database, Azure Data Lake Storage). Azure SQL Database, Azure Data Lake Storage).

Data Engineer 52

Data Engineer Data Engineering Project Coding 52

DataKitchen

JANUARY 25, 2022

DataOps improves the robustness, transparency and efficiency of data workflows through automation. For example, DataOps can be used to automate data integration. Previously, the consulting team had been using a patchwork of ETL to consolidate data from disparate sources into a data lake.

Consulting 97

Consulting Recruitment Data Lake Data Engineer 97

Workfall

JULY 18, 2023

Additionally, they offer a way to keep your transformed data up to date without redoing the entire transformation process. By using DBT materializations, you can streamline your data workflows and focus on gaining insights rather than spending excessive time on repetitive calculations.

Datasets 77

Datasets Entertainment Data Workflow Data 77

Monte Carlo

MAY 14, 2024

Apache ORC (Optimized Row Columnar) : In 2013, ORC was developed for the Hadoop ecosystem to improve the efficiency of data storage and retrieval. This development was crucial for enabling both batch and streaming data workflows in dynamic environments, ensuring consistency and durability in big data processing.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture. It also shows that they can manage data workflows across various Azure services.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

Databand.ai

AUGUST 30, 2023

One key aspect of data orchestration is the automation of data pipeline tasks. By automating repetitive tasks, such as data extraction, transformation, and loading (ETL), organizations can streamline their data workflows and reduce the risk of human error.

Data Pipeline 52

Data Pipeline Data Governance Government Business Analyst 52

Netflix Tech

NOVEMBER 20, 2023

IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. IPS enables users to continue to use the data processing patterns with minimal changes. Note that the backfill support is skipped in this blog.

Process 86

Process Data Pipeline Datasets SQL 86

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Ascend.io

AUGUST 18, 2023

While both are important, there are reasons why production pipeline platforms are often preferred for deploying and managing large-scale applications: Reproducibility: Production pipeline platforms can ensure that the same sequence of data processing and modeling steps are executed consistently.

Data Pipeline 52

Data Pipeline Programming Language Data Science Coding 52

Knowledge Hut

NOVEMBER 17, 2023

cloud Technical Skills for Azure Data Engineers Here I have listed the skills required for an Azure data engineer: 1. Programming and Scripting Languages Proficiency in languages like Python for data manipulation and SQL for database querying, enabling efficient data processing and analysis.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Lake 52

Monte Carlo

JULY 27, 2023

Collaborate with data engineering and development teams to implement data quality best practices and optimize data workflows. Document data quality issues, testing procedures, and resolutions for future reference and knowledge sharing.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Knowledge Hut

MARCH 28, 2024

Salary (Average) $135,094 per year (Source: Talent.com) Top Companies Hiring Deloitte, IBM, Capgemini Certifications Microsoft Certified: Azure Solutions Architect Expert Job Role 3: Azure Big Data Engineer The focus of Azure Big Data Engineers is developing and implementing big data solutions with the use of the Microsoft Azure platform.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

Cloudera

AUGUST 21, 2020

Can you talk about some of the technology that helps make managing live streaming data possible? Cloudera DataFlow offers the capability for Edge to cloud streaming data processing. This type of end-to-end data processing that starts at the Edge and ends in the cloud is made possible by using Apache NiFi.

Banking 62

Banking Kafka Cloud Storage Government 62

phData: Data Engineering

SEPTEMBER 29, 2023

The client needed a partner to set up a cloud data platform and operationalize the new reporting, alerting, and QA environment. phData’s Solution: After an initial training engagement, the client brought phData in to handle both its move to Snowflake and the setup for supporting data workflows.

Data Engineer 52

Data Engineer Data Engineering Engineering Algorithm 52