Cloudera

AUGUST 26, 2021

This means that there is out of the box support for Ozone storage in services like Apache Hive , Apache Impala, Apache Spark, and Apache Nifi, as well as in Private Cloud experiences like Cloudera Machine Learning (CML) and Data Warehousing Experience (DWX). Data ingestion through ‘s3’. Spark SQL to access Hive table.

Data Science 116

Data Science Cloud Hadoop Metadata 116

Knowledge Hut

MARCH 20, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Knowledge Hut

MARCH 15, 2024

The job description for Azure data engineer that I have elucidated below focuses more on foundational tasks while providing opportunities for learning and growth within the field: Data ingestion: This role involves assisting in the process of collecting and importing data from various sources into Azure storage solutions.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

phData: Data Engineering

SEPTEMBER 19, 2023

Cost reduction by minimizing data redundancy, improving data storage efficiency, and reducing the risk of errors and data-related issues. Data Governance and Security By defining data models, organizations can establish policies, access controls, and security measures to protect sensitive data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Data Engineering Podcast

OCTOBER 12, 2020

Are you bogged down by having to manually manage data access controls, repeatedly move and copy data, and create audit reports to prove compliance? Immuta is an automated data governance solution that enables safe and easy data analytics in the cloud.

Business Intelligence 100

Business Intelligence BI Consulting Data Ingestion 100

Monte Carlo

JUNE 2, 2024

Based on our job postings analysis, here are some key areas of expertise to focus on: Technical Expertise Programming Languages: Proficiency in SQL (mentioned in 88% of job postings) and Python (78%) is essential. These languages are used to write efficient, maintainable code and create scripts for automation and data processing.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

Based on our job postings analysis, here are some key areas of expertise to focus on: Technical Expertise Programming Languages: Proficiency in SQL (mentioned in 88% of job postings) and Python (78%) is essential. These languages are used to write efficient, maintainable code and create scripts for automation and data processing.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Snowflake

AUGUST 16, 2023

Data comes in a continuous manner, and often a separate architecture is required to handle streaming data. 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. Learn more here.

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

Knowledge Hut

APRIL 25, 2023

It is one of the most liked data engineering tools of the present day. It allows data engineers to easily build and customize data models and create interactive dashboards and reports. Key features: Interactive data exploration Real-time reporting Easy data modeling 3.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

AltexSoft

AUGUST 29, 2023

These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Common structured data sources include SQL databases like MySQL, Oracle, and Microsoft SQL Server. Semi-structured data sources. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

FEBRUARY 21, 2023

GCP Data Engineer Certification The Google Cloud Certified Professional Data Engineer certification is ideal for data professionals whose jobs generally involve data governance, data handling, data processing, and performing a lot of feature engineering on data to prepare it for modeling.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Monte Carlo

APRIL 24, 2023

Snowpipe and other features makes Snowflake’s inclusion in this top data lake vendors list a no-brainer. Snowflake simplifies data ingestion, querying, and transformation through its built-in support for SQL and compatibility with numerous data processing and integration tools.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Edureka

FEBRUARY 7, 2023

They should also be proficient in programming languages such as Python , SQL , and Scala , and be familiar with big data technologies such as HDFS , Spark , and Hive. Learn programming languages: Azure Data Engineers should have a strong understanding of programming languages such as Python , SQL , and Scala.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Monte Carlo

JULY 10, 2023

Data can go missing for nearly endless reasons, but here are a few of the most common challenges around data completeness: Inadequate data collection processes Data collection and data ingestion can cause data completion issues when collection procedures aren’t standardized, requirements aren’t clearly defined, and fields are incomplete or missing.

Data Collection 40

Data Collection Data Governance Government Data 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

Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Snowflake

JUNE 6, 2024

Snowflake Notebooks aim to provide a convenient, easy-to-use interactive environment that seamlessly blends Python, SQL and Markdown, as well as integrations with key Snowflake offerings, like Snowpark ML, Streamlit, Cortex and Iceberg tables. Discover valuable business insights through exploratory data analysis.

SQL 93

SQL Python Data Workflow Machine Learning 93

DareData

JANUARY 30, 2023

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. AWS Glue: A fully managed data orchestrator service offered by Amazon Web Services (AWS).

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Data Engineering Weekly

SEPTEMBER 11, 2023

Data Engineering Weekly Is Brought to You by RudderStack RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. But at its core, data governance isn’t about control.

Data Engineering 62

Data Engineering Data Engineer Engineering Kafka 62

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. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability.

Metadata 130

Metadata MongoDB Scala MySQL 130

Monte Carlo

NOVEMBER 14, 2023

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. It is designed to be easily queryable with SQL even for large analytic tables (we’re talking petabytes of data). Limited data type support.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

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. Structured data is modeled to be easily searchable and occupy minimal storage space.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Snowflake

NOVEMBER 29, 2023

While the former can be solved by tokenization strategies provided by external vendors, the latter mandates the need for patient-level data enrichment to be performed with sufficient guardrails to protect patient privacy, with an emphasis on auditability and lineage tracking.

Metadata 86

Metadata Healthcare Medical Data Storage 86

Data Engineering Weekly

NOVEMBER 13, 2022

link] Sponsored: Build SQL Pipelines. With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation. The author narrates how OS-C adopted Data Contract and federated data governance strategy to help fight against climate change. Not Endless DAGs!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Mutt Data

JULY 16, 2023

Challenge Addi had established a functioning analytical data platform, however, they aimed to enhance its capabilities and take it to the next level by implementing a Modern Data Stack. 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 Banking Data Ingestion Data Pipeline 40

Mutt Data

JULY 16, 2023

Challenge Addi had established a functioning analytical data platform, however, they aimed to enhance its capabilities and take it to the next level by implementing a Modern Data Stack. 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 Banking Data Ingestion 40

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? This is frequently referred to as a 5 or 7 layer (depending on who you ask) data stack like in the image below. Here are some of the most common solutions that are involved in modern data pipelines and the role they play.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Snowflake

JUNE 4, 2024

Snowflake offers a secure, streamlined approach to developing across data workloads, reducing costs and reliance on external tools. This means faster development and happier data teams. Explore and experiment with data, visualize results, share insights — all in one place. Let’s dive deeper into what we announced.

Data Pipeline 100

Data Pipeline Python SQL Government 100

phData: Data Engineering

AUGUST 4, 2023

The shared-nothing architecture ensures that users don’t have to worry about distributing data across multiple cluster nodes. Snowflake hides user data objects and makes them accessible only through SQL queries through the compute layer. This includes tasks such as data cleansing, enrichment, and aggregation.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Towards Data Science

JULY 24, 2023

A decade later, a considerable amount of innovation in the data management industry is revolving around how different data platforms are coupling or decoupling storage and compute (don’t worry I include examples in the next section). Why are these things related, and more importantly, why should data leaders care?

Metadata 96

Metadata Data Warehouse Data Lake Data Science 96

phData: Data Engineering

FEBRUARY 16, 2023

These projects should include all functional areas within the data platform including analytics engineering, machine learning , and data science. Data governance and data classification are potential reasons to separate projects in dbt Cloud. Often jobs would be scheduled as part of a data ingestion activity.

Cloud 52

Cloud Database Data Ingestion Coding 52

AltexSoft

JULY 29, 2022

To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases. The most common language for data analysis is SQL but barebone Hadoop doesn’t support it.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 82

Engineering Raw Data Data Science Scala 82

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. Trino is a distributed SQL query engine. Hop onto the repository here: [link] 7.

Big Data 52

Big Data Project Metadata Programming Language 52

phData: Data Engineering

JANUARY 3, 2022

This allows us to create new versions of our data sets, populate them with data, validate our data, and then redeploy our views on top of that data to use the new version of our data. The two most common approaches for data ingestion and processing are batch and streaming.

IT 52

IT AWS Software Engineer Software Engineering 52