Databand.ai

AUGUST 30, 2023

It emphasizes the importance of collaboration between different teams, such as data engineers, data scientists, and business analysts, to ensure that everyone has access to the right data at the right time. 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

Edureka

FEBRUARY 7, 2023

An Azure Data Engineer is responsible for designing, implementing, and maintaining data management and data processing systems on the Microsoft Azure cloud platform. They work with large and complex data sets and are responsible for ensuring that data is stored, processed, and secured efficiently and effectively.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Knowledge Hut

APRIL 25, 2023

The tremendous growth in data generation, then the rise in data engineer jobs - there’s no arguing the fact that the big data industry is at its best pace and you, as an aspiring data engineer, have a lot to learn and make out of it - including some tools! What are Data Engineering Tools?

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 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

ProjectPro

FEBRUARY 21, 2023

With so many data engineering certifications available , choosing the right one can be a daunting task. There are over 133K data engineer job openings in the US, but how will you stand out in such a crowded job market? The answer is- by earning professional data engineering certifications! AWS or Azure?

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Data Engineering Podcast

JUNE 5, 2022

In this episode Sean Falconer explains the idea of a data privacy vault and how this new architectural element can drastically reduce the potential for making a mistake with how you manage regulated or personally identifiable information. Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code.

Data Security 100

Data Security Metadata MongoDB Scala 100

Ascend.io

JANUARY 10, 2024

This process ensures your data is accurate, complete, and consistently up-to-date — preventing any degradation that could mislead your AI, which can lead to erroneous outputs. Effective Data Governance: Lastly, let’s talk about governance. This is your rulebook for managing data. Actionable tip?

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Knowledge Hut

JANUARY 3, 2024

DevOps Engineers ensure smooth integration of development and operations in Azure environments. Azure Data Engineer Azure Data Engineers are experts in the design and implementation of data solutions on Azure. Experience in data modeling, data ingestion, and data transformation.

Cloud Computing 52

Cloud Computing Recruitment Computer Science Education 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Snowflake

AUGUST 16, 2023

What remains challenging is how streaming data is brought together with batch data. That’s why we built Snowpipe Streaming, now generally available to handle row-set data ingestion. This is where Dynamic Tables (currently in public preview) come in.

Scala 52

Scala Transportation Kafka Data Lake 52

DareData

JULY 5, 2023

Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users. This flexibility can lead to valuable insights and opportunities for business growth.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Edureka

FEBRUARY 9, 2023

Azure Data Engineering is a rapidly growing field that involves designing, building, and maintaining data processing systems using Microsoft Azure technologies. Contents: What is the role of an Azure Data Engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 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

DareData

JANUARY 30, 2023

Learn the most important data engineering concepts that data scientists should be aware of. As the field of data science and machine learning continues to evolve, it is increasingly evident that data engineering cannot be separated from it.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

ProjectPro

AUGUST 24, 2021

Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

phData: Data Engineering

JANUARY 3, 2022

What is Data Engineering? Everything You Need to Know in 2022 Nick Goble January 4, 2022 It’s easy to overlook the amount of data that’s being generated every day — from your smartphone, your Zoom calls, to your Wi-Fi-connected dishwasher. Table of Contents What is Data Engineering What is Data Governance?

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 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

Data Engineering Weekly

NOVEMBER 13, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Cloudera

FEBRUARY 15, 2024

By leveraging cutting-edge technology and an efficient framework for managing, analyzing, and securing data, financial institutions can streamline operations and enhance their ability to meet compliance requirements efficiently, while maintaining a strong focus on risk management. This results in enhanced efficiency in compliance processes.

Insurance 88

Insurance Banking Data Architecture Data Ingestion 88

Data Engineering Podcast

JUNE 19, 2022

In order to level up their value a new trend of active metadata is being implemented, allowing use cases like keeping BI reports up to date, auto-scaling your warehouses, and automated data governance. Data teams are increasingly under pressure to deliver. In fact, while only 3.5% That’s where our friends at Ascend.io

Metadata 130

Metadata MongoDB Scala MySQL 130

Monte Carlo

MAY 25, 2023

Picture this: your data is scattered. Data pipelines originate in multiple places and terminate in various silos across your organization. Your data is inconsistent, ungoverned, inaccessible, and difficult to use. Some of the value companies can generate from data orchestration tools include: Faster time-to-insights.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Monte Carlo

NOVEMBER 14, 2023

How data observability can increase data trust in Iceberg tables What is Apache Iceberg? Apache Iceberg is an open source data lakehouse table format developed by the data engineering team at Netflix to provide a faster and easier way to process large datasets at scale. Limited data type support.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Podcast

NOVEMBER 16, 2020

Summary The core mission of data engineers is to provide the business with a way to ask and answer questions of their data. This often takes the form of business intelligence dashboards, machine learning models, or APIs on top of a cleaned and curated data set.

Data Management 100

Data Management Management BI Business Intelligence 100

Mutt Data

JULY 16, 2023

This would be a crucial step in becoming increasingly data-driven, while also enabling them to tackle any data governance concerns or data bottlenecks in their journey to insights. The project included: Setting up Databricks as the place to host the company data lake with delta tables to use as a single source of truth.

Data Lake 40

Data Lake Data Ingestion Banking Data Pipeline 40

Mutt Data

JULY 16, 2023

This would be a crucial step in becoming increasingly data-driven, while also enabling them to tackle any data governance concerns or data bottlenecks in their journey to insights. The project included: Setting up Databricks as the place to host the company data lake with delta tables to use as a single source of truth.

Data Lake 40

Data Lake AWS Data Ingestion Banking 40

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

phData: Data Engineering

AUGUST 4, 2023

Snowflake Stages Use Case Examples Here are a few examples of how Snowflake stages can be used: Data Ingestion from Amazon S3: Snowflake stages can be used to ingest data from Amazon S3 into Snowflake. The data can then be processed using Snowflake’s SQL capabilities.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

AltexSoft

JULY 29, 2022

What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Under the hood, the framework divides a chunk of Big Data into smaller, digestible parts and allocates them across multiple commodity machines to be processed in parallel. How data engineering works under the hood.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

phData: Data Engineering

JANUARY 3, 2022

Software engineering practices define how to reliably and effectively build software and data products, delivering value faster to your customers. In this post, we will explore the complexities involved with software engineering with a focus on data engineering and data operations (DataOps).

IT 52

IT AWS Software Engineer Software Engineering 52

Ascend.io

JULY 26, 2023

Data teams are tasked with the crucial responsibility of transforming raw data into valuable insights, a process that directly influences business outcomes. This article aims to shed light on these challenges that data teams commonly face and offers insights into the practices of successful data teams.

Data Pipeline 52

Data Pipeline Data Governance Data Government 52

Monte Carlo

OCTOBER 5, 2023

Ownership of measurements Different teams can play different roles in measuring data quality, but you’ll want to be sure clear accountability is assigned and good communication channels are in place. Data quality standards for governance Data governance is the process of maintaining the availability, usability, privacy, and security of data.

Government 52

Government Data Governance Data Datasets 52

Snowflake

APRIL 20, 2023

Data Pipelines Snowpipe Streaming – public preview While data generated in real time is valuable, it is more valuable when paired with historical data that helps provide context. Write simple Snowpark programs and deploy them as procedures — great for data engineers building pipelines.

Medical 57

Medical Retail Python Pharmaceutical 57

Monte Carlo

AUGUST 19, 2023

The data platform manages the collection, normalization, transformation, and application of data for a given data product—from business insights and dashboards to ML and AI engineering. So, with all that ambiguity, where’s a data engineer to start?Even We’ll cover: What is a data platform?

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization.

IT 59

IT Data Warehouse Data Governance Data Lake 59

phData: Data Engineering

JULY 18, 2023

Enterprises can effortlessly prepare data and construct ML models without the burden of complex integrations while maintaining the highest level of security. Generally, organizations need to integrate a wide variety of source systems when building their analytics platform, each with its own specific data extraction requirements.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52