Snowflake

JUNE 6, 2024

Schedule data ingestion, processing, model training and insight generation to enhance efficiency and consistency in your data processes. Access Snowflake platform capabilities and data sets directly within your notebooks. We invite you to explore Snowflake Notebooks and discover how it can enhance your data workflows.

SQL 115

SQL Python Machine Learning Data Workflow 115

Data Engineering Podcast

SEPTEMBER 11, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Can you describe what Hevo Data is and the story behind it?

Data Pipeline 130

Data Pipeline Building MongoDB Scala 130

ProjectPro

JUNE 6, 2025

The AWS Data Engineer Associate Certification specifically validates critical tasks, such as data ingestion, transformation, and orchestration, leveraging programming concepts. Step 2: Master the AWS Data Engineer Certification Syllabus Understanding the exam domains and objectives is crucial for effective preparation.

AWS 40

AWS Certification Data Engineer Data Engineering 40

ProjectPro

JUNE 6, 2025

It offers a scalable and flexible solution for data integration that can handle large volumes of data. It also provides an intuitive visual interface for designing and managing complex data workflows, allowing for easy scheduling and monitoring of data pipelines.

Coding 40

Coding Manufacturing Data Cleanse Data Warehouse 40

Data Engineering Podcast

JULY 3, 2022

In order to quickly identify if and how two data systems are out of sync Gleb Mezhanskiy and Simon Eskildsen partnered to create the open source data-diff utility. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. In fact, while only 3.5%

Data Integration 100

Data Integration MongoDB Scala MySQL 100

ProjectPro

JUNE 6, 2025

Step 2: Master Big Data Tools and Technologies Familiarize yourself with the core Big Data technologies and frameworks, such as Hadoop , Apache Spark, and Apache Kafka. These tools are the backbone of Big Data processing and analytics. Apache Kafka: Kafka is a distributed event streaming platform.

Big Data 40

Big Data Hadoop Scala NoSQL 40

ProjectPro

JUNE 6, 2025

Build an ETL Pipeline with Talend for Export of Data from Cloud Learn Efficient Multi-Source Data Processing with Talend ETL Learn How to Implement SCD in Talend to Capture Data Changes Talend Real-Time Project for ETL Process Automation 2. Moreover, automation scripting is crucial for maintaining and monitoring the ETL process.

Data Engineer 40

Data Engineer Data Engineering Engineering ETL Tools 40

Striim

SEPTEMBER 11, 2024

Data Collection/Ingestion The next component in the data pipeline is the ingestion layer, which is responsible for collecting and bringing data into the pipeline. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

ProjectPro

JUNE 6, 2025

From business intelligence to machine learning, Azure Databricks provides a flexible and scalable environment to tackle various data challenges. Working on this project will help you learn the necessary techniques and processes to create and manage live delta tables, enabling real-time data ingestion , updates, and queries.

Data Engineering 40

Data Engineering Data Engineer Engineering Data Lake 40

ProjectPro

JUNE 6, 2025

He explores their collaborative potential in orchestrating, exploring, and analyzing data, shaping a secure and comprehensive data engineering landscape. Its flexibility allows you to easily incorporate changes in data sources, transformations, and destinations.

ETL Tools 40

ETL Tools Data Engineering Data Engineer Data Lake 40

Data Engineering Podcast

SEPTEMBER 4, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. In fact, while only 3.5% That’s where our friends at Ascend.io

MongoDB 100

MongoDB Scala MySQL Data Ingestion 100

ProjectPro

JUNE 6, 2025

Unlike the conventional ETL process, which involves complex data extraction, transformation, and loading steps, Zero ETL directly integrates data from the source to the destination. This integration allows for real-time data processing and analytics, reducing latency and simplifying data workflows.

Data Analytics 40

Data Analytics MySQL PostgreSQL Data Lake 40

ProjectPro

JUNE 6, 2025

Moreover, you can use ADF Service to transform the ingested data to fulfill business requirements. In most Big Data solutions, ADF Service is used as an ETL or ELT tool for data ingestion. Explain the data source in the Azure data factory. Can you list all the activities that can be performed in ADF?

Data Lake 60

Data Lake Metadata SQL Datasets 60

ProjectPro

JUNE 6, 2025

A data science pipeline represents a systematic approach to collecting, processing, analyzing, and visualizing data for informed decision-making. Data science pipelines are essential for streamlining data workflows, efficiently handling large volumes of data, and extracting valuable insights promptly.

Data Science 40

Data Science Building AWS Data Lake 40

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

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. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Ascend.io

DECEMBER 21, 2022

21, 2022 – Ascend.io , The Data Automation Cloud, today announced they have partnered with Snowflake , the Data Cloud company, to launch Free Ingest , a new feature that will reduce an enterprise’s data ingest cost and deliver data products up to 7x faster by ingesting data from all sources into the Snowflake Data Cloud quickly and easily.

Data Ingestion 52

Data Ingestion Google Cloud Data Lake Cloud 52

ProjectPro

JUNE 6, 2025

By working on this project, you will gain a comprehensive understanding of crucial aspects like setting up access permissions to ensure data security, establishing triggers for automated data ingestion to keep your data updated, and implementing transformation logic using Cloud Functions.

Data Integration 40

Data Integration Project Data Lake PostgreSQL 40

Data Engineering Weekly

OCTOBER 30, 2022

Editor’s Note: The current state of the Data Catalog The results are out for our poll on the current state of the Data Catalogs. The highlights are that 59% of folks think data catalogs are sometimes helpful. We saw in the Data Catalog poll how far it has to go to be helpful and active within a data workflow.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

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 Engineer 52

Monte Carlo

APRIL 24, 2023

In this article, we’ll dive deep into the data presentation layers of the data stack to consider how scale impacts our build versus buy decisions, and how we can thoughtfully apply our five considerations at various points in our platform’s maturity to find the right mix of components for our organizations unique business needs.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Databand.ai

AUGUST 30, 2023

Automation plays a critical role in the DataOps framework, as it enables organizations to streamline their data management and analytics processes and reduce the potential for human error. This can be achieved through the use of automated data ingestion, transformation, and analysis tools.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

Raw Data 50

Raw Data Data Ingestion Data Datasets 50

Knowledge Hut

NOVEMBER 2, 2023

Top 10 Azure Data Engineering Project Ideas for Beginners For beginners looking to gain practical experience in Azure Data Engineering, here are 10 Azure Data engineer real time projects ideas that cover various aspects of data processing, storage, analysis, and visualization using Azure services: 1.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

Ascend.io

MAY 31, 2023

Table of Contents The Common Threads: Ingest, Transform, Share Before we explore the differences between the ETL process and a data pipeline , let’s acknowledge their shared DNA. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

Snowflake

MARCH 27, 2023

Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL. Workflows automates not only geospatial processes, but other data workflows as well.

Generalist 52

Generalist Business Analyst Raw Data SQL 52

Monte Carlo

MAY 25, 2023

Data orchestration helps companies comply with various international privacy laws and regulations, many of which require companies to demonstrate the source and rationale for their data collection. As data volume grows, scheduling becomes critical to successfully managing your data ingestion and transformation jobs.

Data Pipeline 52

Data Pipeline Data Workflow Data Governance Data 52

Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

Google Cloud 52

Google Cloud Data Workflow Data Data Engineer 52

Knowledge Hut

SEPTEMBER 25, 2023

Data Engineer Design, implement, and maintain data pipelines for data ingestion, processing, and transformation in Azure. Work together with data scientists and analysts to understand the needs for data and create effective data workflows.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Lake 52

Monte Carlo

DECEMBER 13, 2023

Thanks to Monte Carlo, we can detect these issues very quickly — most of the time, even before our data workflow fails, so users aren’t impacted. Georvic Tur , Senior Data Engineer at Tools for Humanity, uses Performance dashboards to make sure his team’s real-time data ingestions are running correctly.

Data Ingestion 52

Data Ingestion BI SQL Data Warehouse 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Knowledge Hut

SEPTEMBER 29, 2023

Why Should You Get an Azure Data Engineer Certification? Becoming an Azure data engineer allows you to seamlessly blend the roles of a data analyst and a data scientist. One of the pivotal responsibilities is managing data workflows and pipelines, a core aspect of a data engineer's role.

Certification 52

Certification Data Engineer Data Engineering Engineering 52

AltexSoft

MARCH 14, 2023

Data orchestration involves managing the scheduling and execution of data workflows. As for this part, Apache Airflow is a popular open-source platform choice used for data orchestration across the entire data pipeline.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Knowledge Hut

MARCH 28, 2024

Role Level: Intermediate Responsibilities Design and develop big data solutions using Azure services like Azure HDInsight, Azure Databricks, and Azure Data Lake Storage. Implement data ingestion, processing, and analysis pipelines for large-scale data sets.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Warehouse 52

AltexSoft

SEPTEMBER 21, 2023

The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization. This means that Elasticsearch can be easily integrated into different modern data stacks.

Engineering 52

Engineering NoSQL Java Programming Language 52

Ascend.io

MAY 19, 2023

In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today. The modern data stack has become disjointed and complex, slowing data engineering’s productivity and limiting their ability to provide value to the business.

Data Pipeline 52

Data Pipeline Data Engineer Data Engineering Media 52

Ascend.io

MARCH 17, 2023

Data quality rules, or assertions, should be configurable at every processing step in every pipeline and evaluate every data record. Value Catching data problems in real-time avoids costly reruns and delays. It reduces the amount of rework arising from after-the- fact quality reviews and simplifies data workflows.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52