Data Preparation with SQL Cheatsheet
KDnuggets
JUNE 27, 2022
If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?
Build Your Second Brain One Piece At A Time
Data Engineering Podcast
APRIL 28, 2024
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.
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Webinars
How to Optimize the Developer Experience for Monumental Impact
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Leading the Development of Profitable and Sustainable Products
The Future of Data Warehousing
Monte Carlo
JANUARY 16, 2024
In this blog post, we’ll look at six innovations that are shaping the future of the data warehousing, as well as challenges and considerations that organizations should keep in mind. Data lake and data warehouse convergence 2. Easier to stream real-time data 3. Zero-copy data sharing 4.
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52 Webinars
How to Optimize the Developer Experience for Monumental Impact
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Leading the Development of Profitable and Sustainable Products
Redefining Data Engineering: GenAI for Data Modernization and Innovation – RandomTrees
RandomTrees
FEBRUARY 6, 2024
Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.
Accelerate Your Data Mesh in the Cloud with Cloudera Data Engineering and Modak NabuTM
Cloudera
OCTOBER 11, 2021
The platform converges data cataloging, data ingestion, data profiling, data tagging, data discovery, and data exploration into a unified platform, driven by metadata. Modak Nabu automates repetitive tasks in the data preparation process and thus accelerates the data preparation by 4x.
Azure Synapse vs Databricks: 2023 Comparison Guide
Knowledge Hut
SEPTEMBER 26, 2023
Key connectivity features include: Data Ingestion: Databricks supports data ingestion from a variety of sources, including data lakes, databases, streaming platforms, and cloud storage. This flexibility allows organizations to ingest data from virtually anywhere.
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Top 10 Azure Data Engineer Job Opportunities in 2024 [Career Options]
Knowledge Hut
MARCH 28, 2024
Role Level Intermediate Responsibilities Design and develop data pipelines to ingest, process, and transform data. Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB.
Achieving Trusted AI in Manufacturing
Cloudera
JANUARY 30, 2024
Add appropriate contextual data (IT/business data), which is critical in AI analysis of manufacturing data. Eliminate data silos. Data from multiple sources must be centralized and stored on a common data lake so that you will have one source of truth across the value chain.
Are we ready to put AI in the hands of business users? by Caitlin Salt
Scott Logic
APRIL 22, 2024
Zero-code, graphically-edited data preparation tools and BI tools are hardly new to the marketplace, either. The business team will then be able to use their domain knowledge in combination with AI-enhanced BI tooling to quickly and easily visualise the data and the forecasts that the business needs. Have Amazon succeeded?
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Bring Order To The Chaos Of Your Unstructured Data Assets With Unstruk
Data Engineering Podcast
JUNE 17, 2021
Summary Working with unstructured data has typically been a motivation for a data lake. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable.
Power BI System Requirements Specification of 2023
Knowledge Hut
OCTOBER 4, 2023
Others Web Sharepoint list OData feed Active Directory Microsoft Exchange Data Preparation and Transformation Data preparation and transformation is considered the most challenging and time-consuming aspect of the latest Power BI requirements. Some requirements will expand the program's capability in various ways.
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Cloudera Data Platform extends Hybrid Cloud vision support by supporting Google Cloud
Cloudera
MARCH 31, 2021
Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed data lakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. Data Preparation (Apache Spark and Apache Hive) .
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The Good and the Bad of Databricks Lakehouse Platform
AltexSoft
MARCH 30, 2023
What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.
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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly
ProjectPro
FEBRUARY 8, 2023
It offers a simple and efficient solution for data processing in organizations. It offers users a data integration tool that organizes data from many sources, formats it, and stores it in a single repository, such as data lakes, data warehouses, etc., where it can be used to facilitate business decisions.
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What is Data Fabric: Architecture, Principles, Advantages, and Ways to Implement
AltexSoft
AUGUST 22, 2022
A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.
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Cloudera Named a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMS)
Cloudera
DECEMBER 16, 2022
Cloudera has long had the capabilities of a data lakehouse, if not the label. Cloudera enables an open data lakehouse architecture that combines all the flexibility of the data lake with the performance of the data warehouse, so enterprises can use all data — both structured and unstructured.
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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure
DataKitchen
JULY 27, 2023
Azure Synapse Analytics Pipelines: Azure Synapse Analytics (formerly SQL Data Warehouse) provides data exploration, data preparation, data management, and data warehousing capabilities. It provides data prep, management, and enterprise data warehousing tools. It does the job.
Enhancing Content Review: Proactively addressing threats with AutoML
LinkedIn Engineering
DECEMBER 20, 2023
It enables models to stay updated by automatically retraining on incrementally larger and more recent data with a pre-defined periodicity. In content moderation classifier development, there are Data ETL (Export, Transform, Load) pipelines that collect data from various sources and store it in offline locations like a data lake or HDFS.
How to Build a Data Pipeline in 6 Steps
Ascend.io
JANUARY 2, 2024
The goal is to cleanse, merge, and optimize the data, preparing it for insightful analysis and informed decision-making. Destination and Data Sharing The final component of the data pipeline involves its destinations – the points where processed data is made available for analysis and utilization.
Data Scientist vs Data Engineer: Differences and Why You Need Both
AltexSoft
OCTOBER 30, 2021
A data scientist takes part in almost all stages of a machine learning project by making important decisions and configuring the model. Data preparation and cleaning. Final analytics are only as good and accurate as the data they use. Data engineers control how data is stored and structured within those locations.
The Emergence of Real-Time Analytics
Rockset
JUNE 17, 2021
Big tech companies have been able to bridge the gap between user demand and application capabilities because they have the time, money and resources to build and maintain on-premise data architectures. They are loaded into data lakes for storage and indexed in Rockset for real-time analytics.
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How to become Azure Data Engineer I Edureka
Edureka
FEBRUARY 7, 2023
They should also be proficient in programming languages such as Python , SQL , and Scala , and be familiar with big data technologies such as HDFS , Spark , and Hive. Learn programming languages: Azure Data Engineers should have a strong understanding of programming languages such as Python , SQL , and Scala.
15 Sample GCP Projects Ideas for Beginners to Practice in 2023
ProjectPro
OCTOBER 6, 2021
Cloud DataPrep is a data preparation tool that is serverless. All these services help in a better user interface, and with Google Big Query, one can also upload and manage custom data sets. Data Lake using Google Cloud Platform What is a Data Lake?
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Recap of Hadoop News for November
ProjectPro
DECEMBER 6, 2016
News on Hadoop-November 2016 Microsoft's Hadoop-friendly Azure Data Lake will be generally available in weeks. Microsoft's cloud-based Azure Data Lake will soon be available for big data analytic workloads. Azure Data Lake will have 3 important components -Azure Data Lake Analytics, Azure Data Lake Store and U-SQL.
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An AI Chat Bot Wrote This Blog Post …
DataKitchen
DECEMBER 9, 2022
DataOps involves close collaboration between data scientists, IT professionals, and business stakeholders, and it often involves the use of automation and other technologies to streamline data-related tasks. One of the key benefits of DataOps is the ability to accelerate the development and deployment of data-driven solutions.
Azure Data Engineer Interview Questions -Edureka
Edureka
FEBRUARY 7, 2023
One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.
Real-Time Analytics with dbt + Rockset
Rockset
JULY 15, 2021
Rockset was founded to make it easy for developers and data teams to go from real-time data to actionable insights. We designed Rockset to remove many of the barriers teams face while building with real-time data including data preparation, performance tuning and infrastructure management.
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How Rockset Enables SQL-Based Rollups for Streaming Data
Rockset
AUGUST 30, 2021
Apache Kafka has made acquiring real-time data more mainstream, but only a small sliver are turning batch analytics, run nightly, into real-time analytical dashboards with alerts and automatic anomaly detection. The majority are still draining streaming data into a data lake or a warehouse and are doing batch analytics.
SQL 52
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15+ Best Data Engineering Tools to Explore in 2023
Knowledge Hut
APRIL 25, 2023
Power BI Power BI is a cloud-based business analytics service that allows data engineers to visualize and analyze data from different sources. It provides a suite of tools for data preparation, modeling, and visualization, as well as collaboration and sharing.
Has the Data Engineer replaced the Business Intelligence Developer?
Advancing Analytics: Data Engineering
JULY 2, 2019
The Data Science Engineer Let’s start with the original idea of the Data Engineer, the support of Data Science functions by providing clean data in a reliable, consistent manner, likely using big data technologies. I’m going to refer to this role as the Data Science Engineer to differentiate from its current state.
Real-Time Data Stories Powering Gen AI & Large Language Models (LLM)
Striim
JULY 10, 2023
HUMANS ARE THINKING MORE LIKE COMPUTERS Humans are getting smarter, Data Science expertise grows at an impressive rate – but arguably what is fuelling the greatest impact on LLM and Gen AI is the speed and quality of data prepared ready-made for the new clever models and algorithms and ML recipes.
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What Data Engineers Think About - Variety, Volume, Velocity and Real-Time Analytics
Rockset
DECEMBER 9, 2019
Variety One of the biggest advancements in recent years in regards to data platforms is the ability to extract data from storage silos and into a data lake. This obviously introduces a number of problems for businesses who want to make sense of this data because it’s now arriving in a variety of formats and speeds.
Power BI Developer Roles and Responsibilities [2023 Updated]
Knowledge Hut
OCTOBER 30, 2023
Develop a long-term vision for Power BI implementation and data analytics. Data Architecture and Design: Lead the design and development of complex data architectures, including data warehouses, data lakes, and data marts. Define data architecture standards and best practices.
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Data Vault on Snowflake: Feature Engineering and Business Vault
Snowflake
MARCH 30, 2023
A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.
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Data Lake vs. Data Warehouse: Differences and Similarities
U-Next
SEPTEMBER 7, 2022
The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. Structuring data refers to converting unstructured data into tables and defining data types and relationships based on a schema. What is Data Lake? . Athena on AWS. .
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How to Become an Azure Data Engineer in 2023?
ProjectPro
JANUARY 19, 2022
ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse. Different methods are used to store different types of data. It is better to know when to employ a data lake vs. a data warehouse to create data solutions for an organization.
Data Mining Functionalities: Meaning, Frameworks & Examples
Edureka
JANUARY 23, 2023
Once experts identify the problem, they start collecting relevant data from various sources. These are pooled in a central data lake or warehouse and prepared for analysis. Companies use various data mining functionalities to arrive at the solution they desire.
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Snowflake Architecture and It's Fundamental Concepts
ProjectPro
JANUARY 31, 2022
It also offers a unique architecture that allows users to quickly build tables and begin querying data without administrative or DBA involvement. Snowflake is a cloud-based data platform that provides excellent manageability regarding data warehousing, data lakes, data analytics, etc. What Does Snowflake Do?
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Top Data Analytics Certifications to Get in 2024
Knowledge Hut
DECEMBER 26, 2023
AWS Certified Data Analytics – Specialty Certification Overview The AWS Certified Data Analytics - Specialty certification validates skills in AWS data analytics services. It covers topics such as data lakes, ingestion, transformation, analysis, and visualization.
Forge Your Career Path with Best Data Engineering Certifications
ProjectPro
FEBRUARY 21, 2023
Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc.
20 Best Open Source Big Data Projects to Contribute on GitHub
ProjectPro
NOVEMBER 15, 2021
In addition to analytics and data science, RAPIDS focuses on everyday data preparation tasks. Delta Lake Source: Github Delta Lake is an open-source project that allows you to create a Lakehouse design based on data lakes. Refer to the Trino Open Source Repository Here: [link] 15.
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100+ Big Data Interview Questions and Answers 2023
ProjectPro
JANUARY 31, 2023
Big Data Architect Interview Questions and Answers Following are the interview questions for big data architects that will help you ace your next job interview. Explain the data preparation process. Data preparation is one of the essential steps in a big data project. Steps for Data preparation.
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Big Data Analytics: How It Works, Tools, and Real-Life Applications
AltexSoft
MAY 14, 2021
With the ETL approach, data transformation happens before it gets to a target repository like a data warehouse, whereas ELT makes it possible to transform data after it’s loaded into a target system. Data storage and processing. Also, Spark supports machine learning (MLlib), SQL, graph processing (GraphX). Apache Kafka.
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20+ Data Engineering Projects for Beginners with Source Code
ProjectPro
AUGUST 24, 2021
In this project, you will explore the usage of Databricks Spark on Azure with Spark SQL and build this data pipeline. Upload it to Azure Data lake storage manually. Create a Data Factory pipeline to ingest files. There are three stages in this real-world data engineering project. The final step is Publish.
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