Christophe Blefari

NOVEMBER 11, 2022

Kovid wrote an article that tries to explain what are the ingredients of a data warehouse. A data warehouse is a piece of technology that acts on 3 ideas: the data modeling, the data storage and processing engine. The end-game dataset. And he does it well. In the post Kovid details every idea.

BI 130

BI Data Warehouse Data Database 130

ThoughtSpot

NOVEMBER 5, 2024

In the realm of modern analytics platforms, where rapid and efficient processing of large datasets is essential, swift metadata access and management are critical for optimal system performance. It stores all the metadata created within a ThoughtSpot instance to enable efficient querying, retrieval, and management of data objects.

Metadata 52

Metadata PostgreSQL Java Database 52

Pinterest Engineering

SEPTEMBER 26, 2023

While it is blessed with an abundance of data for training, it is also crucial to maintain a high data storage efficiency. Therefore, we adopted a hybrid data logging approach, with which the data is logged through both the backend service and the frontend clients. The process is captured in Figure 1.

Software Engineering 105

Software Engineering Software Engineer Machine Learning Datasets 105

Knowledge Hut

FEBRUARY 29, 2024

Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered. This process of inferring the information from sample data is known as ‘inferential statistics.’ A database is a structured data collection that is stored and accessed electronically.

Data Science 98

Data Science Datasets Machine Learning Database Design 98

Data Engineering Weekly

FEBRUARY 2, 2025

DeepSeek development involves a unique training recipe that generates a large dataset of long chain-of-thought reasoning examples, utilizes an interim high-quality reasoning model, and employs large-scale reinforcement learning (RL). Many articles explain how DeepSeek works, and I found the illustrated example much simpler to understand.

Data Engineer 58

Data Engineer Data Engineering Engineering Data Lake 58

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

Data Engineering Podcast

DECEMBER 16, 2019

Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Databand.ai

MAY 30, 2023

High-quality data is essential for making well-informed decisions, performing accurate analyses, and developing effective strategies. Data quality can be influenced by various factors, such as data collection methods, data entry processes, data storage, and data integration.

Data Cleanse 85

Data Cleanse Datasets Data Governance Data Validation 85

Knowledge Hut

MAY 2, 2024

But, in the majority of cases, Hadoop is the best fit as Spark’s data storage layer. Fault Tolerance: Apache Spark achieves fault tolerance using a spark abstraction layer called RDD (Resilient Distributed Datasets), which is designed to handle worker node failure. count(): Return the number of elements in the dataset.

Hadoop 96

Hadoop Scala Datasets Java 96

Christophe Blefari

JUNE 16, 2023

The power of pre-commit and SQLFluff —SQL is a query programming language used to retrieve information from data storages, and like any other programming language, you need to enforce checks at all times. We don't need spaces ( credits ) Data Economy 🤖 Graphext raises $4.6m This is neat. Telmai raises $5.5m

Programming Language 130

Programming Language SQL PostgreSQL Data 130

AltexSoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. A powerful Big Data tool, Apache Hadoop alone is far from being almighty.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

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

Snowflake

APRIL 18, 2024

Legacy SIEM cost factors to keep in mind Data ingestion: Traditional SIEMs often impose limits to data ingestion and data retention. Snowflake allows security teams to store all their data in a single platform and maintain it all in a readily accessible state, with virtually unlimited cloud data storage capacity.

Data Lake 109

Data Lake Data Ingestion Bytes Cloud Computing 109

Data Engineering Podcast

AUGUST 17, 2020

What is your approach to integrating with the broader ecosystem of data storage and processing utilities? How is the built in data versioning implemented? What is the user experience for interacting with different versions of datasets? How do you manage the lifecycle of versioned data to allow garbage collection?

Data Engineer 100

Data Engineer Data Engineering Engineering Database Design 100

Data Engineering Podcast

AUGUST 14, 2021

Your host is Tobias Macey and today I’m interviewing Davit Buniatyan about Activeloop, a platform for hosting and delivering datasets optimized for machine learning Interview Introduction How did you get involved in the area of data management? Can you describe what Activeloop is and the story behind it?

Unstructured Data 100

Unstructured Data Machine Learning Data Lake SQL 100

Confluent

JULY 3, 2019

In order to achieve our targets, we’ll use pre-built connectors available in Confluent Hub to source data from RSS and Twitter feeds, KSQL to apply the necessary transformations and analytics, Google’s Natural Language API for sentiment scoring, Google BigQuery for data storage, and Google Data Studio for visual analytics.

Data Analysis 21

Data Analysis Kafka Datasets Java 21

Cloudera

MARCH 5, 2024

Iceberg delivers the open table format so that enterprises can put AI to work on their data all in an on-premises setting. This approach brings new compute engines into the fold, adding Spark, Flink, Impala, and NiFi, enabling concurrent access and processing of datasets within Iceberg.

Data Lake 87

Data Lake Data Storage Government Kafka 87

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

Knowledge Hut

DECEMBER 22, 2023

Linear Algebra Linear Algebra is a mathematical subject that is very useful in data science and machine learning. A dataset is frequently represented as a matrix. Statistics Statistics are at the heart of complex machine learning algorithms in data science, identifying and converting data patterns into actionable evidence.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Cloudera

AUGUST 31, 2020

It’s not just about having data; it’s about having the right data at the right time in the right context. . Bernard highlighted a number of compelling examples and use cases and also reinforced the fact that with the pandemic at play, datasets that were relevant in January 2020 are totally useless in August.

Insurance 101

Insurance Manufacturing Retail Banking 101

Pinterest Engineering

MAY 2, 2023

We set up a separate dataset for each event type indexed by our system, because we want to have the flexibility to scale these datasets independently. In particular, we wanted our KV store datasets to have the following properties: Allows inserts. We need each dataset to store the last N events for a user.

Lambda Architecture 89

Lambda Architecture Datasets Software Engineering Software Engineer 89

Cloudera

JANUARY 26, 2022

Network operating systems let computers communicate with each other; and data storage grew—a 5MB hard drive was considered limitless in 1983 (when compared to a magnetic drum with memory capacity of 10 kB from the 1960s). The amount of data being collected grew, and the first data warehouses were developed.

Cloud 87

Cloud Cloud Computing Hadoop Data Warehouse 87

Towards Data Science

JULY 9, 2024

While Parquet based data lake storage, offered by different cloud providers, gave us the immense flexibilities during the initial days of data lake implementations, the evolution of business and technology requirements in current days are posing challenges around those implementations.

Data Lake 73

Data Lake Datasets Data Storage Database 73

Knowledge Hut

JUNE 26, 2023

From analysts to Big Data Engineers, everyone in the field of data science has been discussing data engineering. When constructing a data engineering project, you should prioritize the following areas: Multiple sources of data (APIs, websites, CSVs, JSON, etc.)

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

Cloudera

JUNE 26, 2023

It can provide a complete solution for data exploration, data analysis, data visualization, viz applications, and model deployment at scale. Impala works best for analytical performance with properly designed datasets (well-partitioned, compacted). Monitoring: should I use WXM or Cloudera Manager?

ETL Tools 76

ETL Tools Programming Language Datasets Professional Services 76

Knowledge Hut

DECEMBER 22, 2023

Big Data Technologies: Familiarize yourself with distributed computing frameworks like Apache Hadoop and Apache Spark. Learn how to work with big data technologies to process and analyze large datasets. Data Management: Understand databases, SQL, and data querying languages. Who can Become Data Scientist?

Portfolio 98

Portfolio Data Science Programming Language Scala 98

Knowledge Hut

MARCH 27, 2024

Examples MySQL, PostgreSQL, MongoDB Arrays, Linked Lists, Trees, Hash Tables Scaling Challenges Scales well for handling large datasets and complex queries. Scales efficiently for specific operations within algorithms but may face challenges with large-scale data storage.

Database 52

Database Relational Database Algorithm Data Storage 52

Lyft Engineering

NOVEMBER 29, 2023

One of ClickHouse’s standout factors is its high performance—due to a combination of factors such as column-based data storage & processing, data compression, and indexing. This is primarily used to export our marketplace health derived datasets for quick slice and dice in determining marketplace health.

Kafka 106

Kafka Data Ingestion Architecture Datasets 106

Sync Computing

JANUARY 27, 2025

For example, when processing a large dataset, you can add more EC2 worker nodes to speed up the task. Amazon S3 : Highly scalable, durable object storage designed for storing backups, data lakes, logs, and static content. Data is accessed over the network and is persistent, making it ideal for unstructured data storage.

AWS 52

AWS Data Lake Big Data Machine Learning 52

dbt Developer Hub

OCTOBER 3, 2024

Apache Iceberg is a high-performance open table format developed for modern data lakes. It was designed for large-scale datasets, and within the project, there are many ways to interact with it. It’s something we wanted to fix, but people should be able to not pay attention and just work with their data.

Metadata 86

Metadata Data Lake Data Storage Accessibility 86

Netflix Tech

SEPTEMBER 18, 2024

The Key-Value Service The KV data abstraction service was introduced to solve the persistent challenges we faced with data access patterns in our distributed databases. This approach balances the need to retrieve large volumes of data while meeting stringent Service Level Objectives (SLOs) for performance and reliability.

Bytes 104

Bytes Metadata Database Data 104

AltexSoft

OCTOBER 30, 2021

Regardless of the structure they eventually build, it’s usually composed of two types of specialists: builders, who use data in production, and analysts, who know how to make sense of data. Distinction between data scientists and engineers is similar. Data scientist’s responsibilities — Datasets and Models.

Data Engineer 98

Data Engineer Data Engineering Engineering Machine Learning 98

Hevo

FEBRUARY 23, 2023

As businesses continue to generate massive amounts of data, the need for efficient and scalable data storage and analysis solutions becomes increasingly important. Two popular options for data warehousing are Google BigQuery and Azure Synapse Analytics, both of which offer powerful features for processing large datasets.

Data Storage 52

Data Storage Datasets Process Data 52

Knowledge Hut

MAY 30, 2024

The Role of Big Data Analytics in the Industrial Internet of Things ScienceDirect.com Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights!

Computer Science 98

Computer Science Data Mining Algorithm Machine Learning 98

Knowledge Hut

APRIL 23, 2024

The history of big data takes people on an astonishing journey of big data evolution, tracing the timeline of big data. The Emergence of Data Storage and Processing Technologies A data storage facility first appeared in the form of punch cards, developed by Basile Bouchon to facilitate pattern printing on textiles in looms.

Big Data 52

Big Data Amazon Web Services Cloud Computing Media 52

Knowledge Hut

DECEMBER 26, 2023

According to the World Economic Forum, the amount of data generated per day will reach 463 exabytes (1 exabyte = 10 9 gigabytes) globally by the year 2025. These skills are essential to collect, clean, analyze, process and manage large amounts of data to find trends and patterns in the dataset.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

AltexSoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. What is Big Data analytics? Big Data analytics processes and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78