Ascend.io

FEBRUARY 28, 2024

The rise of generative AI is changing more than just technology; it’s reshaping our professional landscapes — and yes, data engineering is directly experiencing the impact. How does AI recalibrate the workload and priorities of data teams? How can data engineers harness the power of AI?

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

Big Data 78

Big Data Data Management Management Metadata 78

phData: Data Engineering

SEPTEMBER 19, 2023

Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. It provides a blueprint for organizing and representing data to enable efficient querying, analysis, and interpretation. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Monte Carlo

FEBRUARY 12, 2024

To differentiate and expand the usefulness of these models, organizations must augment them with first-party data – typically via a process called RAG (retrieval augmented generation). Today, this first-party data mostly lives in two types of data repositories.

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Monte Carlo

FEBRUARY 26, 2024

A data catalog is a constantly updated inventory of the universe of data assets within an organization. It uses metadata to create a picture of the data, as well as the relationships between data assets of diverse sources, and the processing that takes place as data moves through systems.

Metadata 52

Metadata Government Data Data Governance 52

Monte Carlo

AUGUST 25, 2023

Modern companies are ingesting, storing, transforming, and leveraging more data to drive more decision-making than ever before. Data teams need to balance the need for robust, powerful data platforms with increasing scrutiny on costs. Teams using a data warehouse usually leverage SQL queries for analytics use cases.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Cloudera

OCTOBER 7, 2021

Within the context of a data mesh architecture, I will present industry settings / use cases where the particular architecture is relevant and highlight the business value that it delivers against business and technology areas. Introduction to the Data Mesh Architecture and its Required Capabilities.

Architecture 124

Architecture Metadata Government Kafka 124

Cloudera

SEPTEMBER 15, 2022

Apache Ozone is a distributed, scalable, and high-performance object store , available with Cloudera Data Platform (CDP), that can scale to billions of objects of varying sizes. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases. Diversity of workloads.

Systems 87

Systems Hadoop Metadata Telecommunication 87

AltexSoft

AUGUST 22, 2022

What’s more, Gartner identifies data fabric implementation as one of the top strategic technology trends for 2022 and expects that by 2024, data fabric deployments will increase the efficiency of data use while halving human-driven data management tasks. What is data fabric? Data fabric architecture example.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

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. Cloud computing has made it easier for businesses to move their data to the cloud for better scalability, performance, solid integrations, and affordable pricing.

AWS 52

AWS Data Management ETL Tools Management 52

Monte Carlo

APRIL 24, 2023

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. 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.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

JUNE 7, 2021

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. As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. You don’t need to archive or clean data before loading.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Monte Carlo

JULY 13, 2023

To effectively manage and analyze this data, data engineering teams must navigate a wide range of challenges, including data access, security, compliance, and data observability. Automation is a missing link in many organizations’ efforts toward data operationalization. Automating self-service access.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Towards Data Science

MARCH 5, 2024

Introduction In the field of data warehousing, there’s a universal truth: managing data can be costly. But let me give you a magical spell to appease the dragon: burn data, not money! At its core, BigQuery is a serverless Data Warehouse for analytical purposes and built-in features like Machine Learning ( BigQuery ML ).

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. In a nutshell, the lakehouse system leverages low-cost storage to keep large volumes of data in its raw formats just like data lakes. Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

MAY 12, 2023

Of course, tapping into this immense pool of unstructured data may offer businesses a wealth of opportunities to better understand their customers, markets, and operations, ultimately driving growth and success. What is unstructured data? This mess of information is analogous to unstructured data. Social media posts.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Cloudera

JUNE 25, 2021

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. Specification of access conditions for specific users and groups.

Cloud 71

Cloud Data Lake Cloud Storage Metadata 71

Snowflake

JULY 10, 2023

With this new Snowpark capability, data engineers and data scientists can process any type of file directly in Snowflake, regardless if files are stored in Snowflake-managed storage or externally. Mike Tuck, Air Pollution Specialist Why unstructured data? ” U.S.

Unstructured Data 40

Unstructured Data Python Process Scala 40

Snowflake

MARCH 30, 2023

Just as a dimensional data model will transform data for human consumption, ML models need raw data transformed for ML model consumption through a process called “ feature engineering.” Data Vault explicitly defines a set of standards called “hard rules” to automate data extraction, cleaning, and modeling into raw vault tables.

Engineering 83

Engineering Raw Data Data Science Scala 83

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

Zalando Engineering

JANUARY 30, 2023

Lastly, by having playbooks in a single location, our Incident Responders and Incident Commanders have easy access to all available emergency procedures in a consistent format. Managing structured data in markdown is not ideal, despite the ability to use front matter for metadata. playbooks per application and ca.

Management 57

Management Metadata Software Engineer Software Engineering 57

Data Engineering Podcast

FEBRUARY 3, 2019

Introduction Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. What pieces of metadata do you track for a given data set?

Digital Media 100

Digital Media Media PostgreSQL Datasets 100

ProjectPro

AUGUST 24, 2016

Table of Contents Need for HBase HBase –Understanding the Basics HBase Architecture Explained Components of Apache HBase Architecture HMaster Region Server Zookeeper Need for HBase Apache Hadoop has gained popularity in the big data space for storing, managing and processing big data as it can handle high volume of multi-structured data.

Architecture 40

Architecture IT Hadoop NoSQL 40

Cloudera

SEPTEMBER 17, 2020

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. . Our solution: Cloudera Data Visualization.

Unstructured Data 81

Unstructured Data Data Warehouse Pharmaceutical MySQL 81

ProjectPro

JANUARY 31, 2023

Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Big data analytics analyzes structured and unstructured data to generate meaningful insights based on changing market trends, hidden patterns, and correlations. RDBMS stores structured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Monte Carlo

APRIL 1, 2021

Over the past few years, data lakes have emerged as a must-have for the modern data stack. But while the technologies powering our access and analysis of data have matured, the mechanics behind understanding this data in a distributed environment have lagged behind. Who has access to it?

Data Lake 52

Data Lake Unstructured Data Data Warehouse Metadata 52

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Is Hadoop a data lake or data warehouse? Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Monte Carlo

NOVEMBER 20, 2023

Data quality is important—no doubt about it. Bad data destroys trust in both the data products and the teams that produce them. But as data quality finally takes its rightful seat at the platform table, where should new data teams start? Data Observability? Nope, me either. Great Expectations?

Government 69

Government Data Data Governance Datasets 69

AltexSoft

JUNE 26, 2023

All successful companies do it: constantly collect data. While today’s world abounds with data, gathering valuable information presents a lot of organizational and technical challenges, which we are going to address in this article. Data collection as the first step in the decision-making process, driven by machine learning.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Towards Data Science

JANUARY 17, 2024

These are key in nearly all data pipelines, allowing for efficient data storage and easier querying and information extraction. They are designed to handle the challenges of big data like size, speed, and structure. Data engineers often face a plethora of choices. io.delta:delta-spark_2.12:3.0.0").config("spark.hadoop.fs.s3a.endpoint",

Big Data 70

Big Data Data Data Storage SQL 70

Ascend.io

JULY 19, 2023

But something about data mesh feels different, doesn’t it? For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle.

Data Governance 40

Data Governance Government Metadata Data 40

Knowledge Hut

DECEMBER 21, 2023

In this article, we will discuss the 10 most popular Hadoop tools which can ease the process of performing complex data transformations. It incorporates several analytical tools that help improve the data analytics process. With the help of these tools, analysts can discover new insights into the data. What is Hadoop?

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

ProjectPro

JANUARY 24, 2023

With over 20 pre-built connectors and 40 pre-built transformers, AWS Glue is an extract, transform, and load (ETL) service that is fully managed and allows users to easily process and import their data for analytics. You can leverage AWS Glue to discover, transform, and prepare your data for analytics.

AWS 52

AWS Data Lake ETL Tools Scala 52

Cloudera

MAY 17, 2021

Modernizing your data warehousing experience with the cloud means moving from dedicated, on-premises hardware focused on traditional relational analytics on structured data to a modern platform. You also want to be sure your data warehouse isn’t locked to today, but is part of a growing, enterprise data cloud. .

Data Warehouse 89

Data Warehouse Cloud Government Metadata 89

Netflix Tech

MARCH 5, 2019

While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. In the Reliability space, our data teams focus on two main approaches.

Cloud 73

Cloud Building Amazon Web Services Metadata 73

RandomTrees

SEPTEMBER 6, 2020

Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS). It stands out among the other enterprise data warehouses by providing lot of features. It is a SaaS offering, and hence it makes it a lot more flexible than traditional data warehouse offerings.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Hepta Analytics

FEBRUARY 14, 2022

We looked at the following: How do we ingest – ETL vs ELT Where do we store the data – Data lake vs data warehouse Which tool to we use to ingest – cronjob vs workflow engine NOTE : This weeks task requires good internet speed and good compute. It is mostly used when you have multiple sources of data.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52