Knowledge Hut

DECEMBER 26, 2023

Data Engineers are engineers responsible for uncovering trends in data sets and building algorithms and data pipelines to make raw data beneficial for the organization. This job requires a handful of skills, starting from a strong foundation of SQL and programming languages like Python , Java , etc.

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Christophe Blefari

JANUARY 14, 2023

In the end Python and SQL are still here for good. On the data processing side there is Polars, a DataFrame library that could replace pandas. With this release you can really mix Python and SQL code. Seek AI promise is a prompt where you ask your data anything and the AI responds on top of the raw data directly.

Python 130

Python Kafka Data Scala 130

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. How ELT Works The process of ELT can be broken down into the following three stages: 1. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

dbt Developer Hub

JUNE 30, 2020

When your data is small enough, this is the preferred approach, however it isn’t scalable. Because dbt is primarily designed for batch-based data processing, you should not schedule your dbt jobs to run continuously. Lambda views are a simple and readily available solution that is tool agnostic and SQL based.

SQL 52

SQL Lambda Architecture Raw Data Architecture 52

Edureka

AUGUST 2, 2023

R’s popularity in the data science community is also evident through its active community support and continuous development of new packages and techniques. 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.

Programming Language 52

Programming Language Programming Scala Pharmaceutical 52

RandomTrees

FEBRUARY 6, 2024

Serving: Delivering Data with Precision: The seamless process significantly enhances the user experience, allowing for intuitive data exploration and decision-making without requiring technical query language knowledge. Generative AI, especially through the use of LLMs, is ushering in a renaissance in data engineering.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Cloud Academy

JANUARY 27, 2022

A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures. Let’s review some of the big picture concepts as well finer details about being a data engineer. Earlier we mentioned ETL or extract, transform, load.

Data Engineering 52

Data Engineering Data Engineer Certification Engineering 52

Knowledge Hut

DECEMBER 22, 2023

Data science uses machine learning algorithms like Random Forests, K-nearest Neighbors, Naive Bayes, Regression Models, etc. They can categorize and cluster raw data using algorithms, spot hidden patterns and connections in it, and continually learn and improve over time. These large data sets are referred to as "Big Data."

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. This flexibility can lead to valuable insights and opportunities for business growth.

Data Lake 52

Data Lake Building Raw Data ETL Tools 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

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. VDK ingests data from the Data Source.

Data Lake 83

Data Lake Data SQL Data Warehouse 83

Ascend.io

AUGUST 31, 2023

Read More: What is ETL? – (Extract, Transform, Load) ELT for the Data Lake Pattern As discussed earlier, data lakes are highly flexible repositories that can store vast volumes of raw data with very little preprocessing. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. They also encourage distributed computation for enhanced query performance and parallel data processing.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

ProjectPro

JANUARY 20, 2016

“I want to work with big data and hadoop. ” How much SQL is required to learn Hadoop? In our previous posts, we have answered all the above questions in detail except “How much SQL is required to learn Hadoop?” Studies found that the de facto language for analysts was SQL.

Hadoop 52

Hadoop SQL Java Big Data 52

Meltano

OCTOBER 5, 2022

DataOps uses a wide range of technologies such as machine learning, artificial intelligence, and various data management tools to streamline data processing, testing, preparing, deploying, and monitoring. This results in a system that gives organizations control over the data flow so that anomalies can be spotted automatically.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

DoorDash Engineering

APRIL 12, 2023

Challenges of ad-hoc SQLs Our initial goal with Curie was to standardize the analysis methodologies and simplify the experiment analysis process for data scientists. Dependency on SQL made it challenging for non-technical users to analyze experiments without assistance from data scientists.

SQL 82

SQL Metadata Raw Data Government 82

Knowledge Hut

JUNE 30, 2023

The practice of designing, building, and maintaining the infrastructure and systems required to collect, process, store, and deliver data to various organizational stakeholders is known as data engineering. You can pace your learning by joining data engineering courses such as the Bootcamp Data Engineer.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Snowflake

MARCH 27, 2023

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL.

Generalist 52

Generalist Raw Data Business Analyst SQL 52

Snowflake

SEPTEMBER 11, 2023

As Snowflake streams define an offset to track change data capture (CDC) changes on underlying tables and views, Tasks can be used to schedule the consumption of that data. You can define a Dynamic Table to include SQL joins between data structures as views, tables or other Dynamic Tables.

SQL 71

SQL Data Raw Data Architecture 71

Knowledge Hut

DECEMBER 28, 2023

An Azure Data Engineer is responsible for designing, implementing and managing data solutions on Microsoft Azure. The Azure Data Engineer certification imparts to them a deep understanding of data processing, storage and architecture.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Ascend.io

SEPTEMBER 14, 2023

PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. Here’s how Python stacks up against SQL, Java, and Scala based on key factors: Feature Python SQL Java Scala Performance Offers good performance which can be enhanced using libraries like NumPy and Cython.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

ProjectPro

DECEMBER 7, 2021

Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

DECEMBER 23, 2022

In ETL, all the transformations are done before the data is loaded into a destination system. For example, Online Analytical Processing (OLAP) systems only allow relational data structures so the data has to be reshaped into the SQL-readable format beforehand. ELT allows them to work with the data directly.

Process 52

Process Building Raw Data Data Lake 52

Knowledge Hut

MARCH 27, 2024

The collection of meaningful market data has become a critical component of maintaining consistency in businesses today. A company can make the right decision by organizing a massive amount of raw data with the right data analytic tool and a professional data analyst. Why Is Big Data Analytics Important?

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Semi-structured data sources. Raw data store section.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

AltexSoft

NOVEMBER 10, 2021

So, unlike data warehouses, the lakehouse system can store and process lots of varied data at a lower cost, and unlike data lakes, that data can be managed and optimized for SQL performance. Data warehouse vs data lake vs data lakehouse: What’s the difference. Data lake.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

MAY 12, 2023

A fixed schema means the structure and organization of the data are predetermined and consistent. It is commonly stored in relational database management systems (DBMSs) such as SQL Server, Oracle, and MySQL, and is managed by data analysts and database administrators. Unstructured data has the potential to grow exponentially.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Knowledge Hut

APRIL 25, 2024

Data Engineering Data generation, storage, maintenance, usage, and distribution are all managed by the computer science field known as data engineering. Big data processing is aided by using computer languages like Python, SQL, and R. It is conceivably the part of data science that requires the most time.

Data Science 52

Data Science Telecommunication Transportation Big Data 52

Knowledge Hut

NOVEMBER 28, 2023

Data engineers design, manage, test, maintain, store, and work on the data infrastructure that allows easy access to structured and unstructured data. Data engineers need to work with large amounts of data and maintain the architectures used in various data science projects. Technical Data Engineer Skills 1.Python

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. SQL Server Integration Services (SSIS): You know it; your father used it. Azure Blob Storage serves as the data lake to store raw data.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Knowledge Hut

OCTOBER 8, 2023

You need to know the data warehousing concepts to make your job easy. You must be proficient in NoSQL and SQL for data engineers to help with database management. Data pipeline design - It's where you extract raw data from different data sources and export it for analysis.

AWS 52

AWS Data Engineering Data Engineer Engineering 52

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses.

AWS 98

AWS Scala Metadata Data Lake 98