Ascend.io

AUGUST 16, 2023

As enterprise usage of data analytics grows, the field has become a significant area of IT expenditure. The collection and preparation of data used for analytics are achieved by building data pipelines that ingest raw data and transform it into useful formats leveraging cloud data platforms like Snowflake, Databricks, and Google BigQuery.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Knowledge Hut

NOVEMBER 28, 2023

The contemporary world experiences a huge growth in cloud implementations, consequently leading to a rise in demand for data engineers and IT professionals who are well-equipped with a wide range of application and process expertise. Data Engineer certification will aid in scaling up you knowledge and learning of data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

MARCH 7, 2024

The greatest data processing challenge of 2024 is the lack of qualified data scientists with the skill set and expertise to handle this gigantic volume of data. Inability to process large volumes of data Out of the 2.5 quintillion data produced, only 60 percent workers spend days on it to make sense of it.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

Hepta Analytics

FEBRUARY 14, 2022

When the business intelligence needs change, they can go query the raw data again. ELT: source Data Lake vs Data Warehouse Data lake stores raw data. The purpose of the data is not determined. The data is easily accessible and is easy to update. x+ and set minimum memory to 5GB.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Maxime Beauchemin

JANUARY 7, 2018

In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. This post distills fragments of wisdom accumulated while working at Yahoo, Facebook, Airbnb and Lyft, with the perspective of well over a decade of data warehousing and data engineering experience.

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Data Engineering Data Engineer Data Process Process 183

Monte Carlo

FEBRUARY 9, 2023

For those unfamiliar, data vault is a data warehouse modeling methodology created by Dan Linstedt (you may be familiar with Kimball or Imon models ) created in 2000 and updated in 2013. Data vault collects and organizes raw data as underlying structure to act as the source to feed Kimball or Inmon dimensional models.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

ProjectPro

SEPTEMBER 26, 2021

According to a survey, big data engineering job interviews increased by 40% in 2020 compared to only a 10% rise in Data science job interviews. Table of Contents Big Data Engineer - The Market Demand Who is a Big Data Engineer? Most of these are performed by Data Engineers.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How data observability can increase data trust in Iceberg tables What is Apache Iceberg? How Apache Iceberg tables structure metadata.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

Data Engineering Podcast

DECEMBER 16, 2019

The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Monte Carlo

AUGUST 25, 2023

One advantage of data warehouses is their integrated nature. As fully managed solutions, data warehouses are designed to offer ease of construction and operation. A warehouse can be a one-stop solution, where metadata, storage, and compute components come from the same place and are under the orchestration of a single vendor.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

MARCH 28, 2023

If you don’t like change, data engineering is not for you. The most prominent, recent examples are Snowflake and Databricks disrupting the concept of the database and ushering in the modern data stack era. As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Towards Data Science

FEBRUARY 9, 2024

Selecting the right data store solution for each aspect of the Data Lake is crucial, but the overarching technology decision involves tying together and exploring these stores to transform raw data into downstream insights. Transformation Few tools have revolutionized data engineering like dbt.

Building 79

Building Transportation Data Lake Metadata 79

DataKitchen

AUGUST 9, 2021

The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.

Pharmaceutical 98

Pharmaceutical Raw Data Data Lake Data 98

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. How Does AWS Glue Work?

AWS 98

AWS Scala Metadata Data Lake 98

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. The overarching philosophy of a data mesh is that it’s designed with data products in mind.

Metadata 52

Metadata Datasets Data Governance Government 52

Monte Carlo

JULY 25, 2023

What is data curation? Data curation is the process of transforming and enriching larger amounts of raw data into smaller, more widely accessible subsets of data that provide additional value to the organization or the intended use case. It would also make the data engineering team a bottleneck.

Raw Data 52

Raw Data Data Warehouse Data Architecture 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

DataKitchen

MAY 4, 2023

The data industry is now adopting similar principles, such as data testing instead of test-driven development, data observability instead of observability, and functional data engineering instead of functional programming. Chris talks about the idea of a ‘domain’ as a principle of Data Mesh.

Raw Data 52

Raw Data Data Datasets Metadata 52

Ascend.io

AUGUST 31, 2023

Read More: What is ETL? – (Extract, Transform, Load) ELT for the Data Lake Pattern As discussed earlier, data lakes are highly flexible repositories that can store vast volumes of raw data with very little preprocessing. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Based on Tecton blog So is this similar to data engineering pipelines into a data lake/warehouse?

Engineering 83

Engineering Raw Data Data Science Scala 83

DataKitchen

SEPTEMBER 21, 2023

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

ProjectPro

FEBRUARY 16, 2023

ETL is a critical component of success for most data engineering teams, and with teams harnessing it with the power of AWS, the stakes are higher than ever. Data Engineers and Data Scientists require efficient methods for managing large databases, which is why centralized data warehouses are in high demand.

AWS 52

AWS Data Management ETL Tools Management 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

Cloudera

MAY 19, 2021

This year, we expanded our partnership with NVIDIA , enabling your data teams to dramatically speed up compute processes for data engineering and data science workloads with no code changes using RAPIDS AI. Data Ingestion. The raw data is in a series of CSV files. P2 GPU instances are not supported.

Machine Learning 137

Machine Learning Datasets Data Science Raw Data 137

DoorDash Engineering

APRIL 12, 2023

As we mentioned in our previous blog , we began with a ‘Bring Your Own SQL’ method, in which data scientists checked in ad-hoc Snowflake (our primary data warehouse) SQL files to create metrics for experiments, and metrics metadata was provided as JSON configs for each experiment.

SQL 82

SQL Metadata Raw Data Government 82

Monte Carlo

JUNE 28, 2022

Over the past several years, cloud data lakes like Databricks have gotten so powerful (and popular) that according to Mordor Intelligence , the data lake market is expected to grow from $3.74 Traditionally, data lakes held raw data in its native format and were known for their flexibility, speed, and open source ecosystem.

Data Lake 52

Data Lake Metadata AWS Data Warehouse 52

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Towards Data Science

DECEMBER 23, 2023

Photo by Ian Taylor on Unsplash This tutorial guides you through an analytics use case, analyzing semi-structured data with Spark SQL. We’ll start with the data engineering process, pulling data from an API and finally loading the transformed data into a data lake (represented by MinIO ).

SQL 72

SQL Data Analytics Hadoop Raw Data 72

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

AltexSoft

NOVEMBER 10, 2021

In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

Databand.ai

JULY 28, 2022

In general, it makes it possible to identify errors in data, reduce the risk associated with system and process changes, and increase trust in data. All of these are essential at a time when data plays such an integral role in business outcomes and decision-making. This technique focuses directly on the data (vs.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Snowflake

APRIL 5, 2023

You can see how Data Vault overcomes some limitations of the dimensional model below: Why Data Vault can be a better choice for CQR and management data warehousing In the CQR, data quality and accuracy are critical. Metadata in the Data Vault approach helps to track the origin and processing of data.

Data Warehouse 52

Data Warehouse Healthcare Unstructured Data Metadata 52

Monte Carlo

MAY 19, 2022

In his seminal 2017 blog post, The Downfall of the Data Engineer , Maxime Beauchemin wrote that the data engineer had the worst seat at the table. So, if the data engineer no longer has the worst seat at the table, who then on the data team has inherited this unfortunate title?

Generalist 52

Generalist Data Governance Government Metadata 52

Workfall

AUGUST 14, 2023

These tools, when combined, form a dynamic duo that streamlines and scales data engineering workflows. In this blog, we will explore the benefits of using DBT and Airflow together and how they make data pipeline management easier than ever before. Customise settings such as the database connection and executor type.

Data Pipeline 62

Data Pipeline Data Raw Data Database 62

Databand.ai

AUGUST 30, 2023

In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability. Please note! Apache Airflow.

Process 52

Process Building Raw Data Data Lake 52

Monte Carlo

APRIL 1, 2021

Here’s where data catalogs fall short and how data discovery platforms and tools can help ensure your data lake doesn’t turn into a data swamp. Data discovery tools and platforms can help. Data catalogs serve as an inventory of metadata and provide information about data health, accessibility, and location.

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Data Lake Unstructured Data Data Warehouse Metadata 52