Knowledge Hut

APRIL 25, 2024

Big data is a term that refers to the massive volume of data that organizations generate every day. In the past, this data was too large and complex for traditional data processing tools to handle. There are a variety of big data processing technologies available, including Apache Hadoop, Apache Spark, and MongoDB.

Big Data 98

Big Data Technology Hadoop NoSQL 98

Confluent

JUNE 18, 2019

Using SQL to run your search might be enough for your use case, but as your project requirements grow and more advanced features are needed—for example, enabling synonyms, multilingual search, or even machine learning—your relational database might not be enough. relational databases) and storing them in an intermediate broker.

Architecture 97

Architecture Building Kafka Database-centric 97

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

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data. professionals often debate the merits of SQL vs. .”-said

NoSQL 49

NoSQL Big Data SQL Database-centric 49

Hevo

SEPTEMBER 20, 2023

Amazon RDS, with its support for the PostgreSQL database, is a popular choice for businesses looking for reliable relational database services. However, the increasing need for advanced analytics and large-scale data processing requires migrating data to more efficient platforms like Databricks.

PostgreSQL 52

PostgreSQL Relational Database Database Data 52

Hevo

AUGUST 18, 2023

Integrating MySQL on Amazon RDS to Azure Synapse can offer a seamless data pipeline, enabling you to leverage the strengths of both for enhanced data processing and analytics. Amazon RDS offers a fully-managed and scalable relational database service, providing seamless deployment.

MySQL 52

MySQL Relational Database Data Pipeline Database 52

Hevo

AUGUST 17, 2023

In the modern data-driven business landscape, extracting actionable insights from available data helps improve performance and growth. Amazon RDS (Relational Database Service) is a popular choice for a fully-managed cloud environment to manage MySQL databases. Let’s look […]

MySQL 52

MySQL Relational Database Database Cloud 52

Confluent

SEPTEMBER 25, 2019

What’s forgotten is that the rise of this paradigm was driven by a particular type of human-facing application in which a user looks at a UI and initiates actions that are translated into database queries. Our goal at Confluent is to help make this happen. Jay Kreps is the CEO of Confluent as well as one of the co-creators of Apache Kafka.

Database-centric 56

Database-centric Kafka Pipeline-centric Retail 56

Striim

SEPTEMBER 11, 2024

Furthermore, Striim also supports real-time data replication and real-time analytics, which are both crucial for your organization to maintain up-to-date insights. By efficiently handling data ingestion, this component sets the stage for effective data processing and analysis. Are we using all the data or just a subset?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Knowledge Hut

JULY 26, 2023

It also has strong querying capabilities, including a large number of operators and indexes that allow for quick data retrieval and analysis. Database Software- Other NoSQL: NoSQL databases cover a variety of database software that differs from typical relational databases. Columnar Database (e.g.-

Database 52

Database NoSQL MongoDB Telecommunication 52

Cloudera

NOVEMBER 2, 2020

An AdTech company in the US provides processing, payment, and analytics services for digital advertisers. Data processing and analytics drive their entire business. But an important caveat is that ingest speed, semantic richness for developers, data freshness, and query latency are paramount. General Purpose RTDW.

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

Grouparoo

DECEMBER 26, 2021

What are the Different Types of Database Implementations? Relational Databases A relational database organizes data into tables that contain links between data elements that define their relationships. This allows quick access to information based on the connections between data elements.

Database 52

Database NoSQL Relational Database Data Storage 52

Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Edureka

AUGUST 2, 2023

SQL Structured Query Language, or SQL, is used to manage and work with relational databases. It is a crucial tool for data scientists since it enables users to create, retrieve, edit, and delete data from databases.SQL (Structured Query Language) is indispensable when it comes to handling structured data stored in relational databases.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Knowledge Hut

MARCH 14, 2024

Based on the needs of your application, Azure SQL Databases can be deployed using various methods. In this article, I will cover the various aspects of Azure SQL Database. What is Azure SQL Database? It is compatible with spatial, JSON, XML, and relational data structures. Let's get right to it.

Database 52

Database SQL Relational Database BI 52

ProjectPro

FEBRUARY 8, 2023

AWS Glue is a widely-used serverless data integration service that uses automated extract, transform, and load ( ETL ) methods to prepare data for analysis. It offers a simple and efficient solution for data processing in organizations. where it can be used to facilitate business decisions. You can use Glue's G.1X

AWS 98

AWS Scala Metadata Data Lake 98

AltexSoft

MAY 14, 2021

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. This data isn’t just about structured data that resides within relational databases as rows and columns. For that purpose, different data processing options exist.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Hevo

MAY 3, 2024

While AWS RDS Oracle offers a robust relational database solution over the cloud, Databricks simplifies big data processing with features such as automated scheduling and optimized Spark clusters.

AWS 40

AWS Relational Database Big Data Database 40

Knowledge Hut

MARCH 19, 2024

It allows businesses to construct event-driven architectures and microservices in which functions are invoked by events like file uploads, database changes, or HTTP requests. Lambda usage includes real-time data processing, communication with IoT devices, and execution of automated tasks.

AWS 52

AWS Cloud Computing Amazon Web Services Relational Database 52

Knowledge Hut

JANUARY 24, 2024

Supports numerous data sources It connects to and fetches data from a variety of data sources using Tableau and supports a wide range of data sources, including local files, spreadsheets, relational and non-relational databases, data warehouses, big data, and on-cloud data.

BI 98

BI Business Intelligence Non-relational Database Machine Learning 98

ProjectPro

OCTOBER 28, 2015

The major difference between Sqoop and Flume is that Sqoop is used for loading data from relational databases into HDFS while Flume is used to capture a stream of moving data. Table of Contents Hadoop ETL tools: Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools What is Sqoop in Hadoop?

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Knowledge Hut

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. According to recent studies, the global database market will grow from USD 63.4

Database 52

Database SQL Process NoSQL 52

Knowledge Hut

NOVEMBER 17, 2023

Using Snowball helps users access large-scale data transmissions and cut down on network costs, long transfer times, and security concerns. Database Amazon Relational Database Service (RDS) Amazon Relational Database Service (RDS) is easy to establish and run on a relational database in the cloud.

AWS 52

AWS Amazon Web Services PostgreSQL Relational Database 52

ProjectPro

JANUARY 31, 2023

Big Data is a collection of large and complex semi-structured and unstructured data sets that have the potential to deliver actionable insights using traditional data management tools. Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Booking.com Engineering

DECEMBER 2, 2022

This feature eliminates code to parse data, lowers our technical debt, and shortens our development time. A Unified View for Operational Data We kept most of our operational data in relational databases, like MySQL. Fig 2: An overview of BigQuery’s disaggregation of storage, memory, and compute[13].

Systems 52

Systems Cloud MySQL Relational Database 52

Confluent

OCTOBER 10, 2019

Most scenarios require a reliable, scalable, and secure end-to-end integration that enables bidirectional communication and data processing in real time. Confluent Platform and Confluent Cloud are already used in many IoT deployments, both in Consumer IoT and Industrial IoT (IIoT).

Kafka 20

Kafka Google Cloud Architecture Machine Learning 20

Cloudera

AUGUST 31, 2021

Case Study: Accenture’s Experience on Legacy Data Warehouse Migration into Cloudera with a Health Insurance Company . Business Problem & Background.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82

Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Cloud Academy

JANUARY 27, 2022

These fundamentals will give you a solid foundation in data and datasets. Knowing SQL means you are familiar with the different relational databases available, their functions, and the syntax they use. Have knowledge of regular expressions (RegEx) It is essential to be able to use regular expressions to manipulate data.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Silectis

JANUARY 3, 2021

BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions. Big Data Large volumes of structured or unstructured data. Big Query Google’s cloud data warehouse. Cassandra A database built by the Apache Foundation.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Towards Data Science

MARCH 5, 2024

This comes with the advantages of reduction of redundancy, data integrity and consequently, less storage usage. Photo by Shubham Dhage on Unsplash While data normalization holds merit in traditional relational databases, the paradigm shifts when dealing with modern analytics platforms like BigQuery.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

Knowledge Hut

APRIL 23, 2024

It is designed to support business intelligence (BI) and reporting activities, providing a consolidated and consistent view of enterprise data. Data warehouses are typically built using traditional relational database systems, employing techniques like Extract, Transform, Load (ETL) to integrate and organize data.

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. HBase Overview : HBase is a Java-based, non-relational, column-oriented, NoSQL distributed database management system that works on top of HDFS.

Hadoop 52

Hadoop Big Data NoSQL Unstructured Data 52

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Knowledge Hut

DECEMBER 26, 2023

They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers. Generalists They are typically responsible for every step of the data processing, starting from managing and making analysis and are usually part of small data-focused teams or small companies.

Data Engineering 98

Data Engineering Data Engineer Engineering Hadoop 98

Knowledge Hut

NOVEMBER 17, 2023

Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Precisely

MARCH 14, 2023

With this module, you’ll be able to: Leverage capabilities from across our data portfolio – meaning access to solutions you already know and rely on in a SaaS-based design infrastructure. Design in the cloud, deploy anywhere – with an array of deployment options for complex data processes. Bigger, better results.

Data Integration 52

Data Integration High Quality Data BI Data 52