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 Engineering 97

Snowflake

JULY 22, 2024

From there, you can address more complex use cases, such as creating a 360-degree view of customers by integrating systems across CRM, ERP, marketing applications, social media handles and other data sources. Snowflake governance capabilities help you uphold and enforce data integrity, compliance and security policies.

Government 119

Government Data Cloud Hadoop 119

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 101

Metadata Data Pipeline Scala Data Process 101

Ascend.io

JULY 3, 2024

This methodology emphasizes automation, collaboration, and continuous improvement, ensuring faster, more reliable data workflows. With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. Let’s dive in!

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineering 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. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Ascend.io

SEPTEMBER 6, 2024

AI-driven data quality workflows deploy machine learning to automate data cleansing, detect anomalies, and validate data. Integrating AI into data workflows ensures reliable data and enables smarter business decisions. Data quality is the backbone of successful data engineering projects.

Data Cleanse 52

Data Cleanse Data Workflow Data Pipeline Machine Learning 52

Ascend.io

JULY 3, 2024

Start small, then scale With data workflows growing in scale and complexity, data teams often struggle to keep up with the increasing volume, variety, and velocity of data. This is where DataOps comes ina methodology designed to streamline and automate data workflows, ensuring faster and more reliable data delivery.

Data Workflow 52

Data Workflow Data Pipeline Data Data Engineering 52

DataKitchen

APRIL 13, 2021

Composable Analytics — A DataOps Enterprise Platform with built-in services for data orchestration, automation, and analytics. Reflow — A system for incremental data processing in the cloud. Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Consulting 121

Consulting Machine Learning Data Science Data Pipeline 121

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

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

Workfall

JUNE 12, 2023

It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape. With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. CDC also plays a crucial role in data integration and ETL processes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

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 Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Striim

AUGUST 14, 2024

We’ll explore their unique advantages, discuss the common challenges you face with traditional data pipelines, and highlight nine key capabilities that set smart data pipelines apart. What is a Smart Data Pipeline? As a result, errors and inconsistencies can be introduced into your data.

Data Pipeline 52

Data Pipeline Data Architecture Transportation 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.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 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

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 Engineering 52

Data Engineering Data Engineer Project Coding 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

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. Is the business logic producing correct outcomes?

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

Edureka

JULY 3, 2024

As data volumes explode across enterprises, the struggle to manage, integrate, and analyze it is getting real. Thankfully, with serverless data integration solutions like Azure Data Factory (ADF), data engineers can easily orchestrate, integrate, transform, and deliver data at scale.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 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 Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

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

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

MARCH 28, 2024

Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance. Develop data models, data governance policies, and data integration strategies. Experience with Azure services for big data processing and analytics.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 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 Engineering Data Engineer Engineering 52

Monte Carlo

JULY 27, 2023

End to end integration testing between multiple independent systems and interfaces (flat files, APIs (ETL), etc. Collaborate with data engineering and development teams to implement data quality best practices and optimize data workflows.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

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

phData: Data Engineering

SEPTEMBER 29, 2023

By utilizing data engineering best practices, a unified approach to comprehensive risk modeling and evaluation ensures consistency in the evaluation and comparison of data. Benefit: Data Engineering streamlines data integration and normalization, providing a precise and accurate risk assessment.

Data Engineering 52

Data Engineering Data Engineer Engineering Algorithm 52

Ascend.io

MARCH 17, 2023

It can constantly detect changes in data and code across the most complex data pipelines, and respond to those changes in real-time. These fingerprint linkages assure that all dependent pipelines maintain data integrity and availability for all users of the data.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineering 52

Ascend.io

MARCH 17, 2023

It can constantly detect changes in data and code across the most complex data pipelines, and respond to those changes in real-time. These fingerprint linkages assure that all dependent pipelines maintain data integrity and availability for all users of the data.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineering 52

AltexSoft

NOVEMBER 7, 2022

DevOps tasks — for example, creating scheduled backups and restoring data from them. Airflow is especially useful for orchestrating Big Data workflows. Airflow is not a data processing tool by itself but rather an instrument to manage multiple components of data processing. When Airflow won’t work.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

RandomTrees

MARCH 17, 2025

Businesses are no longer just collecting data; they are looking to connect it , transform it , and leverage it for valuable insights in real-time. This is where Airbyte , the open-source data integration platform, is redefining the game. Airbyte supports both batch and real-time data integration.

Data Integration 52

Data Integration ETL Tools Data Workflow Data Pipeline 52

Striim

MARCH 13, 2025

Striims vector embedding generator is a significant asset for businesses looking to enhance their real-time data processing capabilities. By embedding vectors directly within the Striim platform, customers can generate meaningful insights without needing additional tools or adding complexity to their workflows.

Data Process 52

Data Process Process Raw Data Machine Learning 52

RandomTrees

MAY 6, 2025

Understanding Modern Data Engineering Modern data engineering refers to the advanced practices and tools that allow an organization to manage large amounts of information in a secure and efficient manner. It incorporates far more than basic data handling techniques.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Cleanse 52