AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. NoSQL databases.

Big Data 78

Big Data Data Analytics IT NoSQL 78

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

U-Next

MARCH 1, 2023

A solid understanding of relational databases and SQL language is a must-have skill, as an ability to manipulate large amounts of data effectively. A good Data Engineer will also have experience working with NoSQL solutions such as MongoDB or Cassandra, while knowledge of Hadoop or Spark would be beneficial.

Data Engineer 75

Data Engineer Data Engineering Non-relational Database Engineering 75

AltexSoft

JUNE 7, 2021

Data storage options. Apache HBase , a noSQL database on top of HDFS, is designed to store huge tables, with millions of columns and billions of rows. Its in-memory processing engine allows for quick, real-time access to data stored in HDFS. Alternatively, you can opt for Apache Cassandra — one more noSQL database in the family.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

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

DareData

JANUARY 30, 2023

For data scientists, these skills are extremely helpful when it comes to manage and build more optimized data transformation processes, helping models achieve better speed and relability when set in production. Examples of NoSQL databases include MongoDB or Cassandra.

Data Engineer 52

Data Engineer Data Engineering NoSQL Engineering 52

Knowledge Hut

DECEMBER 26, 2023

The responsibilities of Data Analysts are to acquire massive amounts of data, visualize, transform, manage and process the data, and prepare data for business communications. In other words, they develop, maintain, and test Big Data solutions.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Striim

SEPTEMBER 11, 2024

In an ETL-based architecture, data is first extracted from source systems, then transformed into a structured format, and finally loaded into data stores, typically data warehouses. This method is advantageous when dealing with structured data that requires pre-processing before storage.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Rockset

JANUARY 5, 2022

While it ensured data integrity, the distributed two-phase lock added a massive delay to SQL database writes — so massive that it inspired the rise of NoSQL databases optimized for fast data writes, such as HBase, Couchbase, and Cassandra. Which is why raw data streams cannot be ingested by traditional rigid SQL databases.

Database 52

Database SQL NoSQL Raw Data 52

Grouparoo

DECEMBER 26, 2021

As data must conform to a defined structural format, future changes to data that affect the structure will require revision of the entire database to reflect the necessary changes. NoSQL Databases A NoSQL database offers an alternative where information structure is nonlinear and non-relational.

Database 52

Database NoSQL Relational Database Data Storage 52

Knowledge Hut

NOVEMBER 3, 2023

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. There are several benefits to MongoDB for data science operations.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Knowledge Hut

APRIL 23, 2024

Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data. Data warehousing offers several advantages. By structuring data in a predefined schema, data warehouses ensure data consistency and accuracy.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Hadoop 52

Knowledge Hut

DECEMBER 21, 2023

NoSQL This database management system has been designed in a way that it can store and handle huge amounts of semi-structured or unstructured data. NoSQL databases can handle node failures. Different databases have different patterns of data storage. Cons : In Avro, the schema is required to read and write data.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Rockset

MARCH 27, 2019

You have complex, semi-structured data—nested JSON or XML, for instance, containing mixed types, sparse fields, and null values. It's messy, you don't understand how it's structured, and new fields appear every so often. Organizations will typically build hard-to-maintain ETL pipelines to feed data into their SQL systems.

Raw Data 40

Raw Data SQL NoSQL Datasets 40

Rockset

DECEMBER 17, 2021

As a key-value NoSQL database, storing and retrieving individual records are its bread and butter. For those unfamiliar, DynamoDB makes database scalability a breeze, but with some major caveats.

SQL 52

SQL NoSQL Database Design Metadata 52

ProjectPro

DECEMBER 17, 2021

are shifting towards NoSQL databases gradually as SQL-based databases are incapable of handling big-data requirements. Industry experts at ProjectPro say that although both have been developed for the same task, i.e., data storage, they vary significantly in terms of the audience they cater to.

Data Science 52

Data Science SQL NoSQL Programming Language 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

DECEMBER 26, 2023

The job of a data engineer is to develop models using machine learning to scan, label and organize this unstructured data. This process helps convert the unstructured data into structured data, which can easily be collected and interpreted using analytical tools.

Data Engineer 98

Data Engineer Data Engineering Engineering Hadoop 98

Rockset

JULY 28, 2022

MongoDB has grown from a basic JSON key-value store to one of the most popular NoSQL database solutions in use today. Documents in MongoDB can also have complex structures. It is widely supported and provides flexible JSON document storage at scale. It also provides native querying and analytics capabilities.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

ProjectPro

AUGUST 24, 2016

The NOSQL column oriented database has experienced incredible popularity in the last few years. HBase is a NoSQL , column oriented database built on top of hadoop to overcome the drawbacks of HDFS as it allows fast random writes and reads in an optimized way. HBase helps perform fast read/writes.

Architecture 40

Architecture IT Hadoop NoSQL 40

Knowledge Hut

APRIL 25, 2023

Strong programming skills: Data engineers should have a good grasp of programming languages like Python, Java, or Scala, which are commonly used in data engineering. Database management: Data engineers should be proficient in storing and managing data and working with different databases, including relational and NoSQL databases.

Data Engineer 52

Data Engineer Data Engineering Engineering Google Cloud 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).

Big Data 78

Big Data Data Management Management Metadata 78

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge.

Big Data 44

Big Data NoSQL Hadoop Data Lake 44

Edureka

AUGUST 27, 2024

Azure DB usually refers to SQL Database, which is for structured data, while Cosmos DB is for various types of data and is designed to work all over the world. Is Cosmos DB SQL or NoSQL? Cosmos DB is mainly a NoSQL database, which means it doesn’t need a fixed structure for data.

NoSQL 40

NoSQL MongoDB SQL Database 40

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer is someone proficient in a variety of programming languages and frameworks, such as Python, SQL, Scala, Hadoop, Spark, etc. One of the primary focuses of a Data Engineer's work is on the Hadoop data lakes. NoSQL databases are often implemented as a component of data pipelines.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Rockset

DECEMBER 31, 2022

Storage of inconsistent schema items If your data objects are required to be stored in inconsistent schemas, DynamoDB can manage that. This is not possible in the case of DynamoDB since it’s a non-relational database that works better with NoSQL formatted data tables.

Non-relational Database 52

Non-relational Database Healthcare NoSQL Amazon Web Services 52

ProjectPro

SEPTEMBER 9, 2021

Spark SQL, for instance, enables structured data processing with SQL. The tool offers a rich interface with easy usage by offering APIs in numerous languages, such as Python, R, etc. Apache Spark also offers hassle-free integration with other high-level tools. Similarly, GraphX is a valuable tool for processing graphs.

Hadoop 52

Hadoop Big Data Tools Java SQL 52

Knowledge Hut

APRIL 23, 2024

It uses data from the past and present to make decisions related to future growth. Data Type Data science deals with both structured and unstructured data. Business Intelligence only deals with structured data. It is not as flexible as BI data sources always have to be pre-planned.

Business Intelligence 52

Business Intelligence Data Science BI Unstructured Data 52

Cloudera

APRIL 2, 2019

HBase is a distributed, scalable NoSQL database that enterprises use to power applications that need random, real time read/write access to semi-structured data. Apache HBase® is one of many analytics applications that benefit from the capabilities of Intel Optane DC persistent memory.

NoSQL 48

NoSQL Google Cloud Hadoop Machine Learning 48

Edureka

FEBRUARY 8, 2024

Instances like I3 and I4 offer a balance of compute power and storage performance, making them ideal for workloads that demand rapid and consistent access to large volumes of data. These instances contribute to reducing data retrieval times and improving overall system responsiveness.

AWS 52

AWS NoSQL Deep Learning Machine Learning 52

AltexSoft

NOVEMBER 10, 2021

The data in this case is checked against the pre-defined schema (internal database format) when being uploaded, which is known as the schema-on-write approach. Purpose-built, data warehouses allow for making complex queries on structured data via SQL (Structured Query Language) and getting results fast for business intelligence.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

ProjectPro

OCTOBER 15, 2014

Generally data to be stored in the database is categorized into 3 types namely Structured Data, Semi Structured Data and Unstructured Data. 2) Hive Hadoop Component is used for completely structured Data whereas Pig Hadoop Component is used for semi structured data.

Hadoop 52

Hadoop Java Unstructured Data SQL 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

Edureka

FEBRUARY 7, 2023

The main advantage of Azure Files over Azure Blobs is that it allows for folder-based data organisation and is SMB compliant, allowing for use as a file share. For storing structured data that does not adhere to the typical relational database schema, use Azure Tables, a NoSQL storage solution.

Data Engineer 52

Data Engineer Data Engineering Engineering Hadoop 52

Edureka

JULY 4, 2024

Additionally, EMR can integrate with Amazon RDS and Amazon DynamoDB for any relational or NoSQL database requirements that the applications have. Security Security is always a top concern with any data processing solution, and Amazon EMR includes many features to provide security assurance for your data.

AWS 52

AWS Amazon Web Services Hadoop Big Data 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

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