Data Engineering Podcast

JANUARY 7, 2024

Summary Data processing technologies have dramatically improved in their sophistication and raw throughput. Unfortunately, the volumes of data that are being generated continue to double, requiring further advancements in the platform capabilities to keep up.

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Data Process Process Data Lake High Quality Data 147

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

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

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).

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Data Engineering Data Engineer Project Coding 52

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.

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Datasets Entertainment Data Workflow Data 77

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.

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Certification Data Engineering Data Engineer Engineering 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.

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Consulting Recruitment Data Lake Data Engineering 97

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 Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

Knowledge Hut

SEPTEMBER 29, 2023

Microsoft Data Engineer Certification is one such certification which is most sought after by professionals. By combining data from various structured and unstructured data systems into structures, Microsoft Azure Data Engineers will be able to create analytics solutions.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

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.

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Data Engineering Data Engineer Engineering Data Warehouse 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

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 Scala Data Warehouse 52

Workfall

JUNE 12, 2023

and we have now migrated the data from our transactional database to the Snowflake data warehouse. Now we can easily create data streams and create a CDC mechanism in Snowflake. The success is true so let’s now verify this change in our snowflake table.

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Telecommunication Metadata Healthcare Finance 80

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

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Data Warehouse SQL PostgreSQL Database 69

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 59

Banking Kafka Cloud Storage Government 59

ProjectPro

JANUARY 20, 2016

If you want to work with big data , then learning Hadoop is a must - as it is becoming the de facto standard for big data processing. Apache Pig helps SQL server professionals create parallel data workflows. Apache pig eases data manipulation over multiple data sources using a combination of tools.

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Hadoop SQL Java Big Data 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. 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

ProjectPro

DECEMBER 21, 2021

Machine Learning engineers are often required to collaborate with data engineers to build data workflows. Data Engineer Data engineers develop and maintain the data platforms that machine learning and AI systems rely on. In the US, the average annual pay for a machine learning engineer is $133,196.

Machine Learning 52

Machine Learning Engineering Algorithm Computer Science 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.

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Pipeline-centric Google Cloud Data Workflow Python 40

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 Data Pipeline 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.

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Process Data Pipeline Datasets SQL 84

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

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 Pipeline 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.

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Data Pipeline Programming Language Data Science Coding 52

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 Engineering 52

Data Engineering Data Engineer Engineering Algorithm 52

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. They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers.

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Data Engineering Data Engineer Engineering Pipeline-centric 98

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

Ascend.io

MAY 31, 2023

Diverging Paths: The Key Differences While ETL and data pipelines share common processes, they diverge significantly in their capabilities and attributes. Programmability In the context of data processing, programmability refers to the ability to customize and write complex operations.

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Data Pipeline ETL Tools Pipeline-centric Data Warehouse 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.

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Big Data Tools Hadoop Big Data Database-centric 98

Monte Carlo

JUNE 14, 2023

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

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Data Pipeline Architecture Data Lake Data Warehouse 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. .

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Consulting Machine Learning Data Science Data Pipeline 123

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. Beats facilitate data movement from source to destination, which can be either Elasticsearch or Logstash, depending on the use case.

Engineering 52

Engineering NoSQL Programming Language Java 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

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

phData: Data Engineering

JANUARY 3, 2022

We recommend identifying sync points that align with your information architecture so that data currency expectations are known at a governance level. This helps drive requirements and determines the right validation at the right time for the data. Too slow and your system becomes backed up and consumers aren’t seeing their data.

IT 52

IT AWS Software Engineer Software Engineering 52

Snowflake

MARCH 27, 2023

The Flow Diagram tool provides a wide range of analytical and data processing components, including various data sources and sinks, spatial joins and filters, clustering, isoline creation, and tools to leverage spatial indexes like H3 and Quadbin.

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Generalist Raw Data Business Analyst SQL 52