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

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

ProjectPro

JANUARY 31, 2023

This process involves data collection from multiple sources, such as social networking sites, corporate software, and log files. Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. Data Processing: This is the final step in deploying a big data model.

Big Data 52

Big Data Hadoop Relational Database AWS 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. They can be accumulated in NoSQL databases like MongoDB or Cassandra.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

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

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. S3 or GCS), NoSQL databases (e.g. PostgreSQL or MySQL).

SQL 52

SQL MongoDB PostgreSQL NoSQL 52

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

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

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

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). They include NoSQL databases (e.g., MySQL), file stores (e.g., Hadoop), cloud data warehouses (e.g.,

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 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. Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive.

Hadoop 52

Hadoop Project Big Data Healthcare 52

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

FEBRUARY 16, 2023

In fact, approximately 70% of professional developers who work with data (e.g., data engineer, data scientist , data analyst, etc.) According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. use SQL, compared to 61.7%

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 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

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. HBase is a NoSQL database. It is suggested to use standalone real database like PostGreSQL and MySQL.

Hadoop 40

Hadoop Metadata SQL Database 40