AltexSoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? Also, they must have in-depth knowledge of data processing languages like Python, Scala, or SQL.

Data Architect 87

Data Architect Certification Generalist Big Data 87

ProjectPro

SEPTEMBER 26, 2021

Automated tools are developed as part of the Big Data technology to handle the massive volumes of varied data sets. Big Data Engineers are professionals who handle large volumes of structured and unstructured data effectively. A Big Data Engineer also constructs, tests, and maintains the Big Data architecture.

Big Data 52

Big Data 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. Data Engineering Data engineering is a process by which data engineers make data useful.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Knowledge Hut

NOVEMBER 28, 2023

With a plethora of new technology tools on the market, data engineers should update their skill set with continuous learning and data engineer certification programs. What do Data Engineers Do? Let us take a look at the top technical skills that are required by a data engineer first: A. Technical Data Engineer Skills 1.Python

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

JUNE 20, 2023

They transform unstructured data into scalable models for data science. Data Engineer vs Machine Learning Engineer: Responsibilities Data Engineer Responsibilities: Analyze and organize unstructured data Create data systems and pipelines.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Knowledge Hut

MAY 9, 2024

They deploy and maintain database architectures, research new data acquisition opportunities, and maintain development standards. Average Annual Salary of Data Architect On average, a data architect makes $165,583 annually. They manage data storage and the ETL process.

Data Science 52

Data Science Data Architect Data Mining Programming Language 52

Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Knowledge Hut

DECEMBER 29, 2023

A Data Engineer's primary responsibility is the construction and upkeep of a data warehouse. In this role, they would help the Analytics team become ready to leverage both structured and unstructured data in their model creation processes. They construct pipelines to collect and transform data from many sources.

Data Science 98

Data Science Data Mining Deep Learning Programming Language 98

Knowledge Hut

MAY 23, 2024

With this tool, data science professionals can quickly extract and transform data. It allows integrating various data analysis & data-related components for machine learning (ML) and data mining objective by leveraging its modular data pipelining concept.

Data Science 52

Data Science MongoDB Programming Language Hadoop 52

AltexSoft

NOVEMBER 10, 2021

Traditional data warehouse platform architecture. Key data warehouse limitations: Inefficiency and high costs of traditional data warehouses in terms of continuously growing data volumes. Inability to handle unstructured data such as audio, video, text documents, and social media posts. websites, etc.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

ProjectPro

JANUARY 19, 2022

Data engineering is a new and ever-evolving field that can withstand the test of time and computing developments. Companies frequently hire certified Azure Data Engineers to convert unstructured data into useful, structured data that data analysts and data scientists can use.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

ProjectPro

NOVEMBER 14, 2015

Data Integration 3.Scalability Specialized Data Analytics 7.Streaming Such unstructured data has been easily handled by Apache Hadoop and with such mining of reviews now the airline industry targets the right area and improves on the feedback given. Scalability 4.Link Link Prediction 5.Cloud Cloud Hosting 6.Specialized

Hadoop 52

Hadoop Project Big Data Healthcare 52

Knowledge Hut

MARCH 20, 2024

They are responsible for establishing and managing data pipelines that make it easier to gather, process, and store large volumes of structured and unstructured data. Building data pipelines requires using ETL technologies such as Talend, Apache Nifi, and Apache Airflow.

Software Engineer 52

Software Engineer Software Engineering Engineering Java 52

ProjectPro

JANUARY 31, 2023

Big data enables businesses to get valuable insights into their products or services. Almost every company employs data models and big data technologies to improve its techniques and marketing campaigns. Most leading companies use big data analytical tools to enhance business decisions and increase revenues.

Big Data 52

Big Data Hadoop Relational Database AWS 52

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

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes.

Process 52

Process Building Raw Data Data Lake 52

Rockset

NOVEMBER 1, 2018

As Peter Bailis put it in his post , querying unstructured data using SQL is a painful process. We at Rockset have built the first schemaless SQL data platform. This impedance mismatch between dynamically typed languages and SQL's static typing has driven development away from SQL databases and towards NoSQL systems.

SQL 40

SQL NoSQL Programming Language Bytes 40

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering.

Process 69

Process Data Lake Metadata Data Warehouse 69

ProjectPro

JUNE 4, 2015

In our earlier articles, we have defined “What is Apache Hadoop” To recap, Apache Hadoop is a distributed computing open source framework for storing and processing huge unstructured datasets distributed across different clusters. Apache Kafka Use Cases Spotify uses Kafka as a part of their log collection pipeline.

Hadoop 52

Hadoop Architecture IT Java 52

ProjectPro

JANUARY 24, 2023

Additionally, columnar storage allows BigQuery to compress data more effectively, which helps to reduce storage costs. BigQuery enables users to store data in tables, allowing them to quickly and easily access their data. It supports structured and unstructured data, allowing users to work with various formats.

Bytes 52

Bytes Google Cloud Data Warehouse Cloud Storage 52

Edureka

FEBRUARY 7, 2023

8) Difference between ADLS and Azure Synapse Analytics Fig: Image by Microsoft Highly scalable and capable of ingesting and processing enormous amounts of data, Azure Data Lake Storage Gen2 and Azure Synapse Analytics are both available (on a Peta Byte scale). For data access, Synapse SQL, an enhanced version of TSQL, is used.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Knowledge Hut

MARCH 27, 2024

Qubole Using ad-hoc analysis in machine learning, it fetches data from a value chain using open-source technology for big data analytics. Qubole provides end-to-end services in moving data pipelines with reduced time and effort. Multi-source data can be migrated to one location through this tool.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Databand.ai

DECEMBER 13, 2022

We’d be remiss not to share that Joseph was a recent guest on Databand’s MAD Data Podcast , where he discussed ways to keep data systems from becoming unwieldy and shared tips for data teams to manage their data warehouses and keep data pipelines running reliably. You can also watch the video recording.

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

Rockset

SEPTEMBER 3, 2020

PostgreSQL has been gaining a lot of traction recently because of its ability to provide both RDBMS-like and NoSQL-like features which enable data to be stored in traditional rows and columns while also providing the option to store complete JSON objects. This will be read by the service and applied to the data in PostgreSQL.

MongoDB 40

MongoDB PostgreSQL SQL Database 40

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

Knowledge Hut

SEPTEMBER 6, 2023

Over the past decade, the IT world transformed with a data revolution. The rise of big data and NoSQL changed the game. Systems evolved from simple to complex, and we had to split how we find data from where we store it. Skills acquired : Core data concepts. Data storage options. Now, it's different.

Certification 52

Certification Database MongoDB MySQL 52

ProjectPro

AUGUST 11, 2021

Storage Layer: This is a centralized repository where all the data loaded into the data lake is stored. HDFS is a cost-effective solution for the storage layer since it supports storage and querying of both structured and unstructured data. Insights from the system may be used to process the data in different ways.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

AltexSoft

JULY 29, 2022

As a result, today we have a huge ecosystem of interoperable instruments addressing various challenges of Big Data. On top of HDFS, the Hadoop ecosystem provides HBase , a NoSQL database designed to host large tables, with billions of rows and millions of columns. MongoDB: an NoSQL database with additional features.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Striim

FEBRUARY 18, 2025

Enter Striims AI Agents Sentinel and Sherlock: Pioneering AI-Powered Data Governance Striims AI agents, Sentinel and Sherlock, are pioneering tools that bring real-time, AI-powered governance to your data pipelines, increasing security without compromising performance.

Data Governance 52

Data Governance Government NoSQL Unstructured Data 52