Monte Carlo

JULY 13, 2023

Data-driven organizations generate, collect, and store vast amounts of data. 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. Automating self-service access.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Cloudera

JUNE 25, 2021

Many Cloudera customers are making the transition from being completely on-prem to cloud by either backing up their data in the cloud, or running multi-functional analytics on CDP Public cloud in AWS or Azure. The Replication Manager service facilitates both disaster recovery and data migration across different environments.

Cloud 73

Cloud Data Lake Cloud Storage Metadata 73

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?

Unstructured Data 40

Unstructured Data Python Process Scala 40

Data Engineering Podcast

FEBRUARY 3, 2019

In this episode Eric Kansa describes how they process, clean, and normalize the data that they host, the challenges that they face with scaling ETL processes which require domain specific knowledge, and how the information contained in connections that they expose is being used for interesting projects.

Digital Media 100

Digital Media Media PostgreSQL Datasets 100

Knowledge Hut

DECEMBER 21, 2023

They can make optimum use of data of all kinds, be it real-time or historical, structured or unstructured. Since Hadoop tools make it easy for organizations to deal with massive amounts of data, they can manage the task internally without outsourcing it to external specialists. Hive has high latency.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

RandomTrees

SEPTEMBER 6, 2020

There is no hardware to select, install, configure or manage and that makes it ideal for organizations that do not want to dedicate resources for support and maintenance. Ongoing maintenance, management and tuning is handled by Snowflake. Snowflake architecture provides flexibility with big data.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Netflix Tech

MARCH 5, 2019

Finally, provisioning our infrastructure itself is also becoming an increasingly complex task, so our data teams contribute to tools for diagnosis and automation of our cloud capacity management. In the Performance space, our data teams currently focus on the quality of experience on Netflix-enabled devices.

Cloud 73

Cloud Building Amazon Web Services Metadata 73

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

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. Plus, there’s the _delta_log folder.

Big Data 69

Big Data Data Data Storage SQL 69

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 84

Unstructured Data Data Warehouse Pharmaceutical MySQL 84

Grouparoo

JANUARY 11, 2022

When it comes to storing large volumes of data, a simple database will be impractical due to the processing and throughput inefficiencies that emerge when managing and accessing big data. There are two main options available, a data lake and a data warehouse.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Striim

JANUARY 4, 2023

Data contracts tackle this uncertainty and end assumptions by creating a formal agreement. This agreement contains a schema that describes and documents data, which determines who can expose data from your service, who can consume your data, and how you can manage your data. What are data contracts?

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

Ascend.io

AUGUST 31, 2023

Modern platforms like Redshift , Snowflake , and BigQuery have elevated the data warehouse model. The Data Lake Pattern Emerging in contrast to the structured world of warehousing, data lakes cater to the dynamic and diverse nature of modern internet-based applications.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Silectis

JANUARY 3, 2021

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

JANUARY 28, 2020

Aside from video data from each camera-equipped store, Standard deals with other data sets such as transactional data, store inventory data that arrive in different formats from different retailers, and metadata derived from the extensive video captured by their cameras.

Retail 40

Retail Google Cloud Raw Data Data Lake 40

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

Zalando Engineering

JANUARY 30, 2023

In this post, we describe how we structured incident playbooks, and how we manage these across 100+ on-call teams. This structure allows all stakeholders involved in incident response to clearly understand the executed actions and target state of the system to expect. Incident Playbooks - where are we now?

Management 57

Management Metadata Software Engineer Software Engineering 57

Confluent

AUGUST 29, 2019

In an identity/access management application, it’s the relationships between roles and their privileges that matters most. If you’ve found yourself needing to write very large JOIN statements or dealing with long paths through your data, then you are probably facing a graph problem. Relationships act like verbs in your graph.

Kafka 55

Kafka Process Algorithm Cloud 55

Snowflake

NOVEMBER 2, 2023

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. In keeping up with ever-evolving data management needs, we’re announcing new capabilities that support customers across all of these patterns.

Data Lake 100

Data Lake Data Warehouse Cloud Unstructured Data 100

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

Typically, data warehouses work best with structured data defined by specific schemas that organize your data into neat, well-labeled boxes. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Engineering at Meta

MARCH 18, 2024

Users can query using regular expressions on log lines, arbitrary metadata fields attached to logs, and across log files of hosts and services. Logarithm’s data model Logarithm represents logs as a named log stream of (host-local) time-ordered sequences of immutable unstructured text, corresponding to a single log file. in PyTorch).

Engineering 91

Engineering Metadata Architecture Designing 91

Rockset

DECEMBER 22, 2020

We will also need to store this data in Elasticsearch. Whether you have each of these types of data in one index or separate doesn’t matter as we will be accessing them separately and joining them within our application. You may have to manage joins natively within your application meaning more code to write, test, and maintain.

SQL 40

SQL Data MongoDB Aggregated Data 40

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Cloudera

SEPTEMBER 15, 2022

Today’s platform owners, business owners, data developers, analysts, and engineers create new apps on the Cloudera Data Platform and they must decide where and how to store that data. Structured data (such as name, date, ID, and so on) will be stored in regular SQL databases like Hive or Impala databases.

Systems 90

Systems Hadoop Metadata Telecommunication 90

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

AltexSoft

AUGUST 22, 2022

Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? The solution is called a data fabric.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Towards Data Science

MARCH 5, 2024

Summary ∘ Embrace data modeling best practices ∘ Master data operations for cost-effectiveness ∘ Design for efficiency and avoid unnecessary data persistence Disclaimer : BigQuery is a product which is constantly being developed, pricing might change at any time and this article is based on my own experience.

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

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 91

Engineering Raw Data Data Science Scala 91

AltexSoft

NOVEMBER 10, 2021

Well, there’s a new phenomenon in data management that received the name of a data lakehouse. The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. And it’s also an epic combo.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 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 need to have the best price/performance to optimize your cost management.

Data Warehouse 91

Data Warehouse Cloud Government Metadata 91

AltexSoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the data governance market will expand from $1.81

Data Governance 52

Data Governance Government Healthcare Programming 52

Ascend.io

JULY 19, 2023

While the journey will differ from company to company — because of their unique business and data needs — there are fundamental principles that provide a blueprint for action. Consider this your primer to stop overthinking, start acting, and truly harness the power of data mesh. Establish clear data governance policies.

Data Governance 40

Data Governance Government Metadata Data 40

AltexSoft

JUNE 7, 2021

As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. Hadoop hides away the complexities of distributed computing, offering an abstracted API to get direct access to the system’s functionality and its benefits — such as. HDFS master-slave structure. scalability.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

AltexSoft

JUNE 26, 2023

It’s the first and essential stage of data-related activities and projects, including business intelligence , machine learning , and big data analytics. Data gathering also plays a key role in different steps of product management , from product discovery to product marketing.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

JULY 29, 2022

No matter the actual size, each cluster accommodates three functional layers — Hadoop distributed file systems for data storage, Hadoop MapReduce for processing, and Hadoop Yarn for resource management. How HDFS master-slave structure works. Hadoop distributed file system: write once, read many times approach.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Netflix Tech

DECEMBER 14, 2018

A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve. NMDB is built to be a highly scalable, multi-tenant, media metadata system that can serve a high volume of write/read throughput as well as support near real-time queries.

Media 94

Media Database Metadata Data Schemas 94