phData: Data Engineering

SEPTEMBER 19, 2023

This blog will guide you through the best data modeling methodologies and processes for your data lake, helping you make informed decisions and optimize your data management practices. What is a Data Lake? What are Data Modeling Methodologies, and Why Are They Important for a Data Lake?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

Edureka

AUGUST 2, 2023

SQL Structured Query Language, or SQL, is used to manage and work with relational databases. Data scientists use SQL to query, update, and manipulate data. Data scientists can also organize unstructured raw data using SQL so that it can be analyzed with statistical and machine learning methods.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

Monte Carlo

FEBRUARY 27, 2024

Right now, RAG is the essential technique to make GenAI models useful by giving an LLM access to an integrated, dynamic dataset while responding to prompts. The RAG chain starts with a user query, which triggers the system to fetch relevant data from the database.

Data Engineering 59

Data Engineering Data Engineer Engineering High Quality Data 59

AltexSoft

AUGUST 29, 2023

It is a versatile platform for exploring, refining, and analyzing petabytes of information that continually flow in from various data sources. Who needs a data lake? If the intricacies of big data are becoming too much for your existing systems to handle, a data lake might be the solution you’re seeking.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Precisely

MARCH 9, 2023

A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model. Data lakes, in contrast, are designed as repositories for all kinds of information, which might not initially be organized and structured. They are malleable. They can be changed, but not easily.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

Edureka

FEBRUARY 8, 2024

Different instance types offer varying levels of compute power, memory, and storage, which directly influence tasks such as data processing, application responsiveness, and overall system throughput. In-Memory Caching- Memory-optimized instances are suitable for in-memory caching solutions, enhancing the speed of data access.

AWS 52

AWS NoSQL Deep Learning Datasets 52

Silectis

JANUARY 3, 2021

” Artificial Intelligence AI is a broad term used to describe engineered systems that have been taught to do a task that typically requires human intelligence. BI (Business Intelligence) Strategies and systems used by enterprises to conduct data analysis and make pertinent business decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Knowledge Hut

DECEMBER 29, 2023

Career Options: This specialty may prepare you for positions such as: Computer systems analyst Computer network analyst Data scientist Data analyst Data Engineer Data manager D. It separates the hidden links and patterns in the data. Data mining's usefulness varies per sector.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

ProjectPro

FEBRUARY 16, 2023

If you are still wondering whether or why you need to master SQL for data engineering, read this blog to take a deep dive into the world of SQL for data engineering and how it can take your data engineering skills to the next level. Your SQL skills as a data engineer are crucial for data modeling and analytics tasks.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Knowledge Hut

OCTOBER 30, 2023

A Power BI developer is a business intelligence personnel who thoroughly understands business intelligence, data integration, data warehousing, modeling, database administration, and technical aspects of BI systems. Ensure compliance with data protection regulations.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

ProjectPro

DECEMBER 7, 2021

Data Pipeline Tools AWS Data Pipeline Azure Data Pipeline Airflow Data Pipeline Learn to Create a Data Pipeline FAQs on Data Pipeline What is a Data Pipeline? In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

ProjectPro

OCTOBER 15, 2014

Directly leverages SQL and is easy to learn for database experts. Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization Hadoop technology is the buzz word these days but most of the IT professionals still are not aware of the key components that comprise the Hadoop Ecosystem.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

ProjectPro

JUNE 6, 2015

It also detects any problems that might be there in the data – so there is some data quality layer. There is Master Function Data and Meta Function Data. Then there is the Access layer to bring the reports to the end users. In the data transformation we saw lot of limitation with this kind of BI architecture.

Business Intelligence 40

Business Intelligence Big Data BI Hadoop 40

ProjectPro

JULY 13, 2015

Large commercial banks like JPMorgan have millions of customers but can now operate effectively-thanks to big data analytics leveraged on increasing number of unstructured and structured data sets using the open source framework - Hadoop.

Hadoop 52

Hadoop Big Data Data Analytics Banking 52

Knowledge Hut

MARCH 15, 2024

Making decisions in the database space requires deciding between RDBMS (Relational Database Management System) and NoSQL, each of which has unique features. RDBMS uses SQL to organize data into structured tables, whereas NoSQL is more flexible and can handle a wider range of data types because of its dynamic schemas.

NoSQL 52

NoSQL Database-centric Relational Database PostgreSQL 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

SEPTEMBER 11, 2015

Learning Hadoop will ensure that you can build a secure career in Big Data. Big Data is not going to go away. There will always be a place for RDBMS, ETL, EDW and BI for structured data. But at the pace and nature at which big data is growing, technologies like Hadoop will be very necessary to tackle this data.

Hadoop 52

Hadoop Java BI Big Data 52

ProjectPro

NOVEMBER 14, 2015

Data Integration 3.Scalability Specialized Data Analytics 7.Streaming Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization 1. Data Migration RDBMSs were inefficient and failed to manage the growing demand for current data. Scalability 4.Link

Hadoop 52

Hadoop Project Big Data Healthcare 52

U-Next

SEPTEMBER 7, 2022

Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Warehouse? . Built to make strategic use of data, a Data Warehouse is a combination of technologies and components. Data Warehouse in DBMS: .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

Rockset

DECEMBER 31, 2022

Because of this, standard transactional databases aren’t always the best fit. Instead, databases such as DynamoDB have been designed to manage the new influx of data. DynamoDB is an Amazon Web Services database system that supports data structures and key-valued cloud services.

Non-relational Database 52

Non-relational Database Healthcare Amazon Web Services NoSQL 52

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

AltexSoft

JUNE 26, 2023

It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more. Data integration , on the other hand, happens later in the data management flow. For this task, you need a dedicated specialist — a data engineer or ETL developer.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Data Engineering Weekly

DECEMBER 18, 2022

Given the characteristic, are we having a “Big Data” problem? Can we spin off a machine with all the data stack and run through the analysis? SaaS companies like Salesforce and Zendesk are increasingly processing and emitting sem-structure data.

Data Engineering 52

Data Engineering Data Engineer Engineering Relational Database 52

Knowledge Hut

DECEMBER 28, 2023

Objective C Apple created the high-level, object-oriented programming language Objective-C for their frameworks and operating systems, such as macOS, iOS, and watchOS. It is commonly used for developing operating systems, system software, high-performance applications, and web development.

Programming Language 52

Programming Language Java Python SQL 52

ProjectPro

AUGUST 11, 2021

Data Lake vs Data Warehouse - The Differences Before we closely analyse some of the key differences between a data lake and a data warehouse, it is important to have an in depth understanding of what a data warehouse and data lake is. Data Lake vs Data Warehouse - The Introduction What is a Data warehouse?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Grouparoo

DECEMBER 26, 2021

For data storage, the database is one of the fundamental building blocks. They provide a method for storing information in an organized manner that ensures it remains accessible while providing the mechanisms to protect the integrity, confidentiality, and availability of the information they hold.

Database 52

Database NoSQL Relational Database Data Storage 52

Snowflake

AUGUST 25, 2023

SQL—the standard programming language of relational databases—was not included in these benchmarks. As part of our vision to bring generative AI and LLMs to the data , we are evaluating a variety of foundational models that could serve as the baseline for text-to-SQL capabilities in the Data Cloud.

Coding 77

Coding SQL Data Cleanse Database 77

Data Engineering Weekly

MARCH 24, 2023

Data creation is often the differentiator between the success & the failure of a data team. A business process or workflow engine is a software system that enables businesses to execute well-defined steps to complete a user’s intention. Event Sourcing Change Data Capture [CDC] Outbox pattern 1.

Engineering 67

Engineering Data Transportation Database 67

ProjectPro

JANUARY 27, 2023

If you're wondering how the ETL process can drive your company to a new era of success, this blog will help you discover what use cases of ETL make it a critical component in many data management and analytic systems. Business Intelligence - ETL is a key component of BI systems for extracting and preparing data for analytics.

BI 52

BI ETL Tools Retail Healthcare 52

ProjectPro

JANUARY 25, 2022

When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems. To access the configuration value, use get(key, defaultValue=None). With PySparkSQL, we can also use SQL queries to perform data extraction.

Big Data 52

Big Data Data Process Process Kafka 52

Knowledge Hut

DECEMBER 26, 2023

The spectrum of sources from which data is collected for the study in Data Science is broad. These data have been accessible to us because of the advanced and latest technologies which are used in the collection of data. They are required to have deep knowledge of distributed systems and computer science.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

AltexSoft

JUNE 7, 2021

As a result, a Big Data analytics task is split up, with each machine performing its own little part in parallel. Hadoop hides away the complexities of distributed computing, offering an abstracted API to get direct access to the system’s functionality and its benefits — such as. A file stored in the system ?an’t

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

ProjectPro

OCTOBER 28, 2015

Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data. The complexity of the big data system increases with each data source.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 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 AWS Relational Database 52

Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. This comes with the advantages of reduction of redundancy, data integrity and consequently, less storage usage.

Bytes 72

Bytes Google Cloud Cloud Storage Utilities 72

AltexSoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two.

Architecture 52

Architecture Data Lake Medical Datasets 52