Analytics Vidhya

MARCH 7, 2023

Introduction Apache Flume is a tool/service/data ingestion mechanism for gathering, aggregating, and delivering huge amounts of streaming data from diverse sources, such as log files, events, and so on, to centralized data storage. Flume is a tool that is very dependable, distributed, and customizable.

Data Ingestion 216

Data Ingestion Data Storage Hadoop Data 216

Analytics Vidhya

FEBRUARY 2, 2023

Introduction In this constantly growing era, the volume of data is increasing rapidly, and tons of data points are produced every second. Now, businesses are looking for different types of data storage to store and manage their data effectively.

Data Lake 202

Data Lake Data Warehouse Data Storage Data 202

KDnuggets

OCTOBER 30, 2023

A comparative overview of data warehouses, data lakes, and data marts to help you make informed decisions on data storage solutions for your data architecture.

Data Lake 145

Data Lake Data Warehouse Data Storage Data 145

KDnuggets

JULY 11, 2022

Learn about the modern tools for data orchestration, data storage, analytical engineering, batch processing, and data streaming.

Data Engineering 144

Data Engineering Data Engineer Engineering Data Storage 144

Start Data Engineering

SEPTEMBER 4, 2024

Key parts of data systems: 2.1. Data flow design 2.3. Data processing design 2.5. Data storage design 2.7. Introduction If you are trying to break into (or land a new) data engineering job, you will inevitably encounter a slew of data engineering tools. Introduction 2. Requirements 2.2. Conclusion 1.

Data Engineering 130

Data Engineering Data Engineer Engineering Data Storage 130

Start Data Engineering

MAY 9, 2024

Analyze usage and optimize table data storage 3.2.1. Snowflake pricing and settings inheritance model 3. Strategies to reduce Snowflake cost 3.1. Quick wins by changing settings 3.1.1. Update warehouse settings 3.2. Identify expensive queries and optimize them 3.2.1.1. Identify expensive queries with query_history 3.2.1.2.

Data Storage 130

Data Storage Accessible Accessibility Management 130

Cloudera

APRIL 22, 2022

The focus has also been hugely centred on compute rather than data storage and analysis. In reality, enterprises need their data and compute to occur in multiple locations, and to be used across multiple time frames — from real time closed-loop actions, to analysis of long-term archived data.

Data Solutions 137

Data Solutions Amazon Web Services Data Storage Cloud 137

Start Data Engineering

OCTOBER 22, 2021

Data modeling 2.1.1. Data storage 2.2. Data transformation 2.2.1. Data pipeline 2.4. Data analytics 3. Introduction 2. SQL skills 2.1. Gathering requirements 2.1.2. Exploration 2.1.3. Modeling 2.1.4. Transformation types 2.2.1.1. Narrow transformations 2.2.1.2. Wide transformations 2.2.2. Practice 4.

SQL 130

SQL Data Engineering Data Engineer Engineering 130

Simon Späti

NOVEMBER 28, 2018

In my recent blog, I researched OLAP technologies, for this post I chose some open-source technologies and used them together to build a full data architecture for a Data Warehouse system. I went with Apache Druid for data storage, Apache Superset for querying and Apache Airflow as a task orchestrator.

Data Warehouse 130

Data Warehouse Data Storage Data Architecture Architecture 130

Seattle Data Guy

DECEMBER 19, 2022

Whether its helping increase revenue by finding new customers or reducing costs, all of it starts with data.

Data Lake 130

Data Lake Data Warehouse Database Data Storage 130

KDnuggets

OCTOBER 22, 2019

For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

Data Management 123

Data Management Management Data Storage Machine Learning 123

Seattle Data Guy

NOVEMBER 8, 2024

Whether it was moving data from a local database instance to S3 or some other data storage layer. As… Read more The post What Is AWS DMS And Why You Shouldn’t Use It As An ELT appeared first on Seattle Data Guy. It was interesting to see AWS DMS used in this manner. But it’s not what DMS was built for.

AWS 130

AWS IT Data Storage Database 130

Christophe Blefari

MARCH 1, 2023

This switch has been lead by modern data stack vision. In terms of paradigms before 2012 we were doing ETL because storage was expensive, so it became a requirement to transform data before the data storage—mainly a data warehouse, to have the most optimised data for querying.

Data Warehouse 278

Data Warehouse SQL Metadata Raw Data 278

Engineering at Meta

JUNE 12, 2024

Storage We need efficient data-storage solutions to store the vast amounts of data used in model training. This involves investing in high-capacity and high-speed storage technologies and developing new data-storage solutions for specific workloads.

Algorithm 143

Algorithm Data Storage Technology Building 143

Christophe Blefari

FEBRUARY 24, 2023

In order to improve your data infra you should sometimes try to occasionally kill your data stack , chaos engineering is something that helps discover issues. This goes further than being a data-driven enterprise , you have to put in place a framework the puts data measurement at every product choice, resulting in maturity increase.

Kafka 130

Kafka Data Storage Data Lake Data 130

Engineering at Meta

MARCH 12, 2024

Storage Storage plays an important role in AI training, and yet is one of the least talked-about aspects. As the GenAI training jobs become more multimodal over time, consuming large amounts of image, video, and text data, the need for data storage grows rapidly.

Building 145

Building Portfolio Utilities Data Storage 145

Christophe Blefari

SEPTEMBER 28, 2023

Historically, data pipelines were designed with an ETL approach, storage was expensive and we had to transform the data before using it. With the cloud, we got the—false—impression that resources were infinite and cheap, so we switched to ETL by pushing everything into a central data storage.

Big Data 147

Big Data Cloud Storage Hadoop SQL 147

Christophe Blefari

MAY 12, 2023

The StarCoder ( credits ) Fast News ⚡️ Zero ELT could be the death of the modern data stack — Amazon launched this trend a few months ago. For instance Zapier launched Zapier Tables some kind of data storage within your zaps. Mainly it resides in the data storage.

Data 130

Data Data Storage SQL Coding 130

Cloudera

OCTOBER 1, 2020

Such a status has yet to be granted and without which, data transfers between the UK and the EU will not be lawfully permitted post-December 31st 2020. Without an agreed legislative route to allow data storage and processing in the US and EU, the UK Government will be left with one option; storage and processing within the UK only.

Government 131

Government Cloud Data Storage Architecture 131

Knowledge Hut

APRIL 25, 2024

Each of these technologies has its own strengths and weaknesses, but all of them can be used to gain insights from large data sets. As organizations continue to generate more and more data, big data technologies will become increasingly essential. Let's explore the technologies available for big data.

Big Data 98

Big Data Technology Hadoop NoSQL 98

Knowledge Hut

MARCH 7, 2024

Cloud providers can offer you access to the infrastructures such as database services, servers, networks, data management , and data storage. It includes resources such as software, servers, databases, data storage, and networking. Software Cloud service providers offer ready-to-use applications to businesses.

Cloud Computing 98

Cloud Computing Amazon Web Services Cloud Google Cloud 98

Monte Carlo

NOVEMBER 12, 2024

When you click on a show in Netflix, you’re setting off a chain of data-driven processes behind the scenes to create a personalized and smooth viewing experience. As soon as you click, data about your choice flows into a global Kafka queue, which Flink then uses to help power Netflix’s recommendation engine.

Architecture 96

Architecture Data Engineering Data Engineer Engineering 96

Cloudera

MAY 9, 2024

Hybrid Horses for Courses: The Right Cloud for AI from Pilot to Production at Scale Later, on May 14 at 12:40 pm BST , hear from Mark Samson, one of Cloudera’s solutions engineering directors, on whether a data center or cloud deployment is best for your organization’s data platform and architecture.

Banking 108

Banking Data Storage Data Analytics Cloud 108

Cloudera

MARCH 5, 2024

The powerful platform data security and governance layer, Shared Data Experience (SDX) , is a fundamental part of the open data lakehouse, in the data center just as it is in the cloud. Zero Downtime Upgrades Beyond improvements to Iceberg and Ozone, the platform now boasts Zero Downtime Upgrades (ZDU).

Data Lake 92

Data Lake Data Storage Government Kafka 92

Christophe Blefari

SEPTEMBER 25, 2023

I'd say that Iceberg (or table formats) are probably one of the technology that will incrementally change for the better the way we write data pipelines. Providing more control over data storage. Yet I think Iceberg is not yet ready to be widely used ( Python write support still missing, you need Spark).

Data 130

Data Data Warehouse Data Storage Cloud 130

Christophe Blefari

SEPTEMBER 25, 2023

I'd say that Iceberg (or table formats) are probably one of the technology that will incrementally change for the better the way we write data pipelines. Providing more control over data storage. Yet I think Iceberg is not yet ready to be widely used ( Python write support still missing, you need Spark).

Data 130

Data Data Warehouse Data Storage Cloud 130

Knowledge Hut

JANUARY 29, 2024

These servers are primarily responsible for data storage, management, and processing. Cloud Computing addresses this by offering scalable storage solutions, enabling Data Scientists to store and access vast datasets effortlessly. The term cloud is referred to as a metaphor for the internet.

Cloud Computing 98

Cloud Computing Data Science Cloud Amazon Web Services 98

Cloudera

NOVEMBER 1, 2023

Managing the data that represents organizational knowledge is easy for any developer and does not require exhaustive cycles of data science work. Utilizing Pinecone for vector data storage over an in-house open-source vector store can be a prudent choice for organizations.

Machine Learning 115

Machine Learning Data Ingestion Database Architecture 115

Knowledge Hut

JANUARY 18, 2024

Data Warehousing Professionals Within the framework of a project, data warehousing specialists are responsible for developing data management processes across a company. Furthermore, they construct software applications and computer programs for accomplishing data storage and management.

Data Science 98

Data Science Business Analyst Data Architect ETL Method 98

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle.

Machine Learning 134

Machine Learning Data Science Database Building 134

Cloudera

OCTOBER 6, 2022

We see this consistently in the data platform/data storage space. . Replacing redundant data storage is a clear opportunity in this category. We maintain the same information in multiple places because it is used across the enterprise. And of course, these siloes all need to be maintained.

Data Lake 95

Data Lake Machine Learning Data Storage Cloud Computing 95

Data Engineering Weekly

OCTOBER 11, 2024

This centralized model mirrors early monolithic data warehouse systems like Teradata, Oracle Exadata, and IBM Netezza. These systems provided centralized data storage and processing at the cost of agility. This approach offered economies of scale but was inherently rigid, inflexible, and vulnerable to disruptions.

Manufacturing 111

Manufacturing Transportation Data Warehouse Unstructured Data 111

Snowflake

NOVEMBER 29, 2023

For example, the data storage systems and processing pipelines that capture information from genomic sequencing instruments are very different from those that capture the clinical characteristics of a patient from a site. A conceptual architecture illustrating this is shown in Figure 3.

Healthcare 89

Healthcare Metadata Medical Data Storage 89

Striim

SEPTEMBER 11, 2024

Striim, for instance, facilitates the seamless integration of real-time streaming data from various sources, ensuring that it is continuously captured and delivered to big data storage targets. Data storage Data storage follows.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Christophe Blefari

JANUARY 20, 2023

There is an introduction post about DataHub — when you look at what you have to run to launch a data catalog: 4 components and 4 different data storage. Don't be surprised if no ones uses data catalogs. When I think that some people are saying Airflow is complex to launch.

Google Cloud 130

Google Cloud Data Hadoop Machine Learning 130

Engineering at Meta

MARCH 6, 2024

When a third-party user registers on WhatsApp or Messenger, they keep their existing user-visible identifier, and are also assigned a unique, WhatsApp-internal identifier that is used at the infrastructure level (for protocols, data storage, etc.)

Media 135

Media Architecture Metadata Data Storage 135

Data Engineering Weekly

DECEMBER 2, 2023

The paper discusses trade-offs among data freshness, resource cost, and query performance. Ref: [link] In the current state of the data infrastructure, we use a combination of multiple specialized data storage and processing engines to achieve this balance.

IT 85

IT BI AWS Kafka 85