Engineering at Meta

MARCH 18, 2024

Logarithm stores locality of data blocks in a central locality service. We implement this on a hosted, highly partitioned and replicated collection of MySQL instances. Query clusters support interactive and bulk queries on one or more log streams with predicate filters on log text and metadata.

Engineering 94

Engineering Metadata Architecture Designing 94

DareData

JANUARY 30, 2023

Examples of relational databases include MySQL or Microsoft SQL Server. Examples of technologies able to aggregate data in data lake format include Amazon S3 or Azure Data Lake. Some examples include Amazon Redshift, Azure SQL Data Warehouse, and Google BigQuery.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 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). While functional, our current setup for managing tables is fragmented.

Big Data 78

Big Data Data Management Management Metadata 78

ProjectPro

OCTOBER 28, 2015

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., Sqoop hadoop can also be used for exporting data from HDFS into RDBMS.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Knowledge Hut

MARCH 27, 2024

Use Cases Ideal for applications requiring structured storage and retrieval of data, such as in business or web development. Essential in programming for tasks like sorting, searching, and organizing data within algorithms. Supports complex query relationships and ensures data integrity.

Database 52

Database Relational Database Algorithm Data Storage 52

Knowledge Hut

JULY 24, 2023

NoSQL Databases NoSQL databases are non-relational databases (that do not store data in rows or columns) more effective than conventional relational databases (databases that store information in a tabular format) in handling unstructured and semi-structured data.

Database 52

Database NoSQL Management Relational Database 52

ProjectPro

JUNE 23, 2016

Apache Sqoop is a lifesaver for people facing challenges with moving data out of a data warehouse into the Hadoop environment. Sqoop is a SQL to Hadoop tool for efficiently importing data from a RDBMS like MySQL, Oracle, etc. It can also be used to export the data in HDFS and back to the RDBMS.

Hadoop 40

Hadoop MySQL Relational Database Java 40

ProjectPro

JANUARY 31, 2023

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. What is the difference between SQL and MySQL?

Big Data 52

Big Data Hadoop Relational Database AWS 52

Knowledge Hut

FEBRUARY 6, 2024

PowerShell for windows: A cross-platform automation and configuration framework or tool, that deals with structured data, REST APIs and object models. JavaScript: An interpreted scripting language to build websites 9. It has a command-line tool. Good-to-know language: 10. It is used to build simple, reliable and efficient software 3.2

Engineering 98

Engineering Programming Language Amazon Web Services Google Cloud 98

Knowledge Hut

OCTOBER 24, 2023

SQL and SQL Server BAs must deal with the organization's structured data. They ought to be familiar with databases like Oracle DB, NoSQL, Microsoft SQL, and MySQL. BAs can store and process massive volumes of data with the use of these databases.

Business Analyst 98

Business Analyst Business Intelligence SQL Programming Language 98

Silectis

JANUARY 3, 2021

Data Science Data science is a practice that uses scientific methods, algorithms and systems to find insights within structured and unstructured data. Data Visualization Graphic representation of a set or sets of data. Data Warehouse A storage system used for data analysis and reporting.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

JANUARY 5, 2022

Data warehousing emerged in the 1990s, and open-source databases, such as MySQL and PostgreSQL , came into play in the late 90s and 2000s. Let’s not gloss over the fact that SQL, as a language, remains incredibly popular, the lingua franca of the data world. Different flavors of SQL databases have been added over time.

Database 52

Database SQL NoSQL Raw Data 52

Rockset

OCTOBER 20, 2021

Let’s walk through an example workflow for setting up real-time streaming ELT using dbt + Rockset: Write-Time Data Transformations Using Rollups and Field Mappings Rockset can easily extract and load semi-structured data from multiple sources in real-time. PostgreSQL or MySQL). S3 or GCS), NoSQL databases (e.g.

SQL 52

SQL PostgreSQL MongoDB NoSQL 52

AltexSoft

JUNE 26, 2023

From the perspective of data science, all miscellaneous forms of data fall into three large groups: structured, semi-structured, and unstructured. Key differences between structured, semi-structured, and unstructured data. Note, though, that not any type of web scraping is legal.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

ProjectPro

SEPTEMBER 26, 2014

The toughest challenges in business intelligence today can be addressed by Hadoop through multi-structured data and advanced big data analytics. Big data technologies like Hadoop have become a complement to various conventional BI products and services. Big data, multi-structured data, and advanced analytics.

Business Intelligence 40

Business Intelligence Hadoop BI Relational Database 40

Rockset

NOVEMBER 24, 2020

Rockset makes it easier to serve modern data applications at scale and at speed. From personalization and gaming to logistics or IoT, Rockset automatically and continuously ingests and indexes structured and semi-structured data at scale for a solution that supports latency-sensitive queries for real-time analytics.

MySQL 40

MySQL Database Data Warehouse Structured Data 40

Knowledge Hut

JUNE 16, 2023

Data preparation: Because of flaws, redundancy, missing numbers, and other issues, data gathered from numerous sources is always in a raw format. After the data has been extracted, data analysts must transform the unstructured data into structured data by fixing data errors, removing unnecessary data, and identifying potential data.

Programming Language 52

Programming Language Data Science Data Analytics Cloud Computing 52

Knowledge Hut

SEPTEMBER 26, 2023

The basic power BI required skills are: How to connect to various data sources: Extracting data from various databases like SQL Server, MySQL, Oracle, etc. Kmowledge on loading data from Excel, CSV, JSON, and other file formats. Using web services and connecting to APIs and web data sources.

BI 52

BI Business Intelligence Raw Data Data Analysis 52

AltexSoft

AUGUST 29, 2023

Data sources can be broadly classified into three categories. Structured data sources. These are the most organized forms of data, often originating from relational databases and tables where the structure is clearly defined. Semi-structured data sources.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

ProjectPro

MARCH 19, 2015

Relational Databases – The fundamental concept behind databases, namely MySQL, Oracle Express Edition, and MS-SQL that uses SQL, is that they are all Relational Database Management Systems that make use of relations (generally referred to as tables) for storing data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 1- Automating the Lakehouse's data intake.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Rockset

FEBRUARY 25, 2021

For example, you might have to develop a real-time data pipeline using a tool like Kafka just to get the data in a format that allows you to aggregate or join data in a performant manner. Analyze Semi-Structured Data As Is The data feeding modern applications is rarely in neat little tables.

SQL 40

SQL Data Pipeline Kafka Database 40

Knowledge Hut

MARCH 27, 2024

Easily scales up to a large amount of data when it is distributed in small chunks. Easy to implement with MySQL, JSON, and highly flexible. Cassandra Data sets can be retrieved in large quantities using APACHE Cassandra, a distributed database with no SQL engine. The Hadoop Distributed File System (HDFS) provides quick access.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

ProjectPro

NOVEMBER 14, 2015

Tools/Tech stack used: The tools and technologies used for such page ranking using Apache Hadoop are Linux OS, MySQL, and MapReduce. Objective and Summary of the project: With social media sites gaining popularity, it has become quite crucial to handle the security and pattern of various data types of the application.

Hadoop 52

Hadoop Project Big Data Healthcare 52

ProjectPro

FEBRUARY 16, 2023

To analyze big data and create data lakes and data warehouses , SQL-on-Hadoop engines run on top of distributed file systems. The SQL-on-Hadoop platform combines the Hadoop data architecture with traditional SQL-style structured data querying to create a specific analytical application tool.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

AltexSoft

OCTOBER 8, 2021

It is possible to move datasets with incremental loading (when only new or updated pieces of information are loaded) and bulk loading (lots of data is loaded into a target source within a short period of time). MySQL), file stores (e.g., Hadoop), cloud data warehouses (e.g., Pre-built connectors. MongoDB), SQL databases (e.g.,

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

AUGUST 24, 2021

Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization. You will set up MySQL for table creation and migrate data from RDBMS to Hive warehouse to arrive at the solution. The Yelp dataset JSON stream is published to the PubSub topic.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

Knowledge Hut

JANUARY 11, 2024

Data science is the field of study that deals with a huge volume of data using modern technologically driven tools and techniques to find some sort of pattern and derive meaningful information out of it that eventually helps in business and financial decisions. This work is done by financial data scientists.

Finance 93

Finance Data Science Programming Language Machine Learning 93

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Language It is a procedural data flow language. It is suggested to use standalone real database like PostGreSQL and MySQL.

Hadoop 40

Hadoop Metadata SQL Database 40

Monte Carlo

JUNE 28, 2023

These are the world of data and the data warehouse that is focused on using structured data to answer questions about the past and the world of AI that needs more unstructured data to train models to predict the future.

Data Warehouse 52

Data Warehouse Scala Unstructured Data Government 52