Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Storyteller | Data Engineer@Quantiphi | Data Scientist | Tealfeed Creator Program'2022 | LICAP'2022 | Thought Leader@GlobalAIHub | Ex-TCS Digital | Kaggle Expert-3x

Result oriented individual with with 4+ years of experience in data engineering, analysis and modelling in banking, healthcare, sales and e-commerce domain. 

Holding strong statistical and analytical ability to explore business ideas and portray innovative solutions. 

Experienced as a data enthusiast I'm GCP, AWS, Azure Certified and I enjoy working on cloud technologies, inclusive of Big data, Predictive analytics, Machine learning, data modelling and visualization techniques.

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities.

Immensely proud to be a part of Linkedin Creator Accelerator Program '2022 and Tealfeed Creator Program '2022. 

Acknowledged as a Data Science & Women Empowerment Thought leader by Global AI Hub.

♐Optimize performances with well designed SQL Queries!

💥Let's have a look at few of the optimization techniques to follow while storing & querying data. 

🎯It's important to optimization when working with complex queries!! 

♾️Few of the best practices to follow include -

🔰 Using regex expressions : Can help in searching data patterns, modifying or validating data or gathering specific data categories. 

🔰 Indexing : Creating indexes on frequently searched columns can greatly improve query performance.

🔰 Subqueries : Helps in simplifying the complex nested queries by filtering, and Aggregating data effectively, this speeds up the performance if use efficiently. 

🔰 Joins (Order of execution) : Using the appropriate type of join and limiting the number of tables joined can improve performance.

🔰 Understanding query execution plan : This can be used to optimize performance by identifying and addressing any bottlenecks or inefficiencies in the query.
 
🔖Carousel Credits: Kadek Dwi Pradnyana

➡️Few other performance optimization methods are :

✅Proper use of data types
✅Normalization
✅Caching
✅Query Optimization
✅Partitioning
✅Profiling
✅SQL Tuning
✅Maintenance and Monitoring

🌀It's important to note that the specific optimization techniques that will work best for a particular query will depend on the structure of the data and the specific requirements of the query. 

👉There are even a more best practices that I might be missing out at this points, feel free to add your inputs in the comments.. 👇

♂️What in your opinion are the best practices for SQL Performance Optimization? 
Nick Singh 📕🐒 Ankit Bansal Shashank Mishra 🇮🇳 Travis Tang Kedeisha Bryan 

#data #sql #analytics #engineering #computing 

POOJA JAIN

🎯How does the data flow from raw sources to reporting tool for analytics? 

🌀Data Engineer plays an important role in order to process the data into an organized manner. 

♾️ Here's an amazing illustration of data processing from raw to end user‼️

🔰It displays a pipeline process to a series of steps or stages that data goes through from the time it is extracted from a source system to the time it is used for reporting and analysis. 

➡️Process of data flow is a key aspect of data warehousing and is used to ensure that data is cleaned, transformed, and loaded into the data warehouse in a consistent and reliable way.

💯It also animates the process of data from various data sources to getting insights isn't a simple one. 

👉This involves a lots of architecture, pipelining, platform, analysis, visualization, security, modelling and integration to delve into gathering insightful outcomes. 

📍Credits - Airbyte 

👨‍💻The various data blocks it processes the data includes, data lake, data warehouse, data marts with catalog, discovery and lineage data. 

📊This infographics delivers highly valuable information to understand the flow of data. 

🔖To understand the data flow from each point lets under what each block depicts in great detail - 

♐𝐒𝐨𝐮𝐫𝐜𝐞 - Data sources refer to the locations or systems where data is stored or generated. A data source can be a database, a flat file, an application, a website, or any other system that contains data that can be used for analysis or decision-making and helps to gather data by some source of extraction tools. 

♐𝐄𝐓𝐋/𝐄𝐋𝐓 - The process of extracting, transforming and loading the data by applying various cleaning mechanism using various ETL tools or cloud processes. ELT can be faster and more efficient than ETL because the data transformation can be done in parallel, using the processing power of the data warehouse itself.

♐𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 - A data warehouse is a large, centralized repository of data that is specifically designed to support business intelligence and decision-making activities. It typically contains historical data from various sources, such as transactional systems and external data feeds, and is optimized for reporting and analysis.

♐𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 - Both reporting and analytics can be performed using the same data warehouse, but they often require different tools and skill sets. Reports are often generated using business intelligence (BI) tools, while analytics is typically done using specialized analytical software and programming languages like R or Python.

#data #engineering #analytics #businessintelligence #cloudcomputing #dataengineering #dataanalytics #programming #visualization 

🎯Cloud Services by different providers from Google Amazon Microsoft Huawei 

➡️An amazing cheatsheet with insightful services parallely by each cloud provider. 

🔰Credits: Unknown(please tag if you're aware) 

#data #dataengineering #cloud #microsoft #amazon #google 

🔖Data has more to it! 

🔰As the field of data is constantly evolving, the new technologies and techniques are being developed continuously. 

➡️What do you think? 

#data

👉𝐃𝐚𝐭𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐝𝐞𝐬𝐢𝐠𝐧 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬!! 

🌀For any Data Engineer having a robust, scalable, well designed and executable data pipeline is really important.

🔰To get deep into how we design a data pipeline and how many various patterns can a data pipeline be formed into, let's first understand what is a data pipeline? 

📍A data pipeline refers to a set of processes that move data from one system or stage to another, typically involving tasks such as extraction, transformation, and loading (ETL). Process can be automated and run on a schedule or in real-time as per the use case. 

♂️Now what does a Data pipeline design pattern refer to?

♐It's a common approach to creating pipelines that extract, transform, and load data from one or more sources to one or more destinations.

♾️Some common data pipeline design patterns include:

🔘𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝 (𝐄𝐓𝐋): This pattern involves extracting data from various sources, transforming it to fit a target schema, and then loading it into a destination, like data warehouse or a database.

🔘𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐋𝐨𝐚𝐝, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 (𝐄𝐋𝐓): This pattern is similar to ETL, but the data is loaded into the destination first and then transformed in place. It is more efficient when the destination system has powerful transformation capabilities.

🔘𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: This approach involves centralizing all data in a centralized repository, usually in its raw format, with the ability to run batch or real-time processing on the data. Agenda is to store all data in a central location, making it easily accessible and performant.

🔘𝐃𝐚𝐭𝐚 𝐯𝐚𝐮𝐥𝐭: It is a way to model a data warehouse and involves using a central repository for storing data, with separate hubs for storing historical data, links between data elements, and satellite tables.

🔘𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: This involves breaking down the pipeline into smaller, independent services that can be developed, deployed, and scaled independently. It functions in improvising flexibility, scalability and improved fault tolerance.

🔘𝐋𝐚𝐦𝐛𝐝𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Approach combines both batch and real-time data processing to provide a holistic view of data. Here, data is first ingested into a master dataset and is also processed in real-time and stored in a speed layer. Finally both layers are combined by serving layer to generate output.

🔘𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: This pattern involves processing data as it's generated in near real-time. It is essentially a variation of Lambda Architecture, but it processes and reacts to events immediately. 

🎯Image Credits: Informatica 

📊What other data pipeline design approaches are suitable when working with different use cases? Share your opinion in comments. 👇

#data #dataengineering #cloud #analytics #computing #scalability #datamining #engineering #growth #learning #business 

♐Is your organization giving you the liberty to maintain work life balance, with the opportunity to work on interesting projects? 

🔰That's often an underrated question for a lot of folks and have been impacting lives to genuinely love the work they do. 

🎯Isn't mental health important for even folks who either need work life balance or who're workaholics? 

♾️Having a good work-life balance allows individuals to have time to pursue personal interests, spend time with loved ones, and rest and recharge. It also allows them to perform better and make better decision in work and personal life.

➡️ Studies have also found that a good work-life balance can also lead to higher levels of employee engagement, which can lead to better performance, increased creativity, and greater job satisfaction.

👉Let's take a poll LinkedIn and understand the opinion of the folks about work life balance and rigorous burnouts impacting lives. 

#data #linkedin #mentalhealth #growth #projects #opportunity #learning #job 

📝Let's begin with the basics!! 

🔴"𝐏𝐲𝐭𝐡𝐨𝐧 𝟐𝟎𝟐𝟑 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐟𝐨𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐞𝐧𝐭𝐡𝐮𝐬𝐢𝐚𝐬𝐭𝐬"

🤔Are you a Python Developer, Web Developer, Software Engineer, Data Analyst, Machine Learning Engineer or a Data Scientist ⁉️

👉Here's an amazing roadmap for python lovers to have understanding of the following concepts ➖

♂️Data types
♂️Data Structures
♂️Regular Expression
♂️Functions and Methods
♂️Object Oriented Programming
♂️Python external libraries

🏷It's not limited to these but include version controlling and repository hosting services. 

🔰Python programming language is one of the most preferred one in the data field. 

♾️ Be it connecting with enormous data analytics or visualization tools or ETL or cloud services, python works well when integrated with any platform. 

👨‍💻Python is most popular language as it easy to learn, fastest growing and has a lot of features to make tasks simple and automate them as well. 

▶️ Here's an insightful Python Developer Roadmap that extends various features and highlights the important concepts and libraries. 

👉Various data roles where having exposure to Python programming helps include -

🔖#DataAnalytics - Analyse data with interesting libraries such as Pandas, Numpy. 

🔖#DataVisualization - Get interactive visuals using seaborn, matplotlib,  plotly. 

🔖#Machinelearning - Create models with scikitlearn, tensorflow, keras etc. 

🔖#Webscraping - Scrape data using libraries like Scrapy, Beautiful Soup, Requests, Urllib, and Selenium. 

🔖#Dataengineering - Libraries to implement batch processing or streaming pipelines like Spark, Kafka, Flink. 

🔖#Databases - Modules in Python have libraries to work with databases such as SQLite, MongoDB, PostgreSQL, MySQL. 

✔️These aren't just packages but important aspects to understand and utilize the efficiency that Python provides being an open source programming and scripting language 

🤔What more Python libraries you use and for what purpose❓ Share in comments 👇

🎯 Roadmap Credits: Kamran Ahmed 

♐GitHub Repository: https://lnkd.in/dprPyDNH

 📝 Happy learning folks!!🙂 Enjoy Coding!! 👩‍💻

#data #python #programming #github #engineering #coding #softwareengineering #businessintelligence #computing #algorithms #automation #datastructures #science #analytics  #pythonprogramminglanguage #bigdata

🎯Data Storytelling at it's best!!

🔖So many insightful derivations to capture from this amazing informative visual that illustrates storytelling.

🎭Data has so many form to it and with just 100 people on earth this video expresses so many effective statistics that help us understand various meaningful visuals.

📍Have meaningful conversations to derive interesting and effective collaborations!!

♐️The goal is to present data in a way that is clear, concise, and engaging, so that your audience can understand the key points and take action based on your recommendations.

➤Narrate your data so effectively that communications become a phase to enjoy at all times with innovative visualizations.

📊It involves using data to answer questions, uncover patterns, and explore relationships, and then using that information to create a compelling story that resonates with your audience. 

✔️Credit: 100people org

#data #storytelling #analytics #datavisualization #statistics 

♾️Making paths is easy, walking on the road is difficult!! 

➡️Having a Data Engineering Roadmap doesn't makes you a Data Engineer. 

🔖But having appropriate resources and keen efforts towards learning definitely can! 

📊Working as a Data Engineer there aren't just a few skills that might suffice. Whereas it's an ocean to learn and grow the deeper you uncover and explore. 

♐Important skills and specialization of people whom you must follow to grow as a data engineer ➖

🔰Python Programming - Real Python by Dan Bader 
🔰SQL - Ankit Bansal and Nick Singh 📕🐒  
🔰Cloud and DevOps - Sandip Das 
🔰AWS - Viktoria Semaan 
🔰Google Cloud - Priyanka Vergadia & Cassie Kozyrkov  
🔰Microsoft Azure -Deepak Goyal
🔰Data governance - George Firican 💡

🎯Including these few important concepts that data engineers must learn includes when working on building ETL pipelines be it for batch processing or streaming ➖

♂️Hadoop
♂️BigData
♂️Storage and Processing
♂️Spark
♂️Kafka
♂️NoSql
♂️Orchestration

👉Data Engineers whom you must follow for informative tips and experiences ➖
Shashank Mishra 🇮🇳  
Zach Wilson 
Benjamin Rogojan 

🔘Willing to begin with working on some hands on data engineering projects,  explore Darshil Parmar's YouTube project list with explanation. 

📍With enormous amount of resources available widely I’m sharing few important resources, one can refer ➖

✅Data Engineering with Python by Packt publishing - https://lnkd.in/dP9hVDcB

✅Python data cleaning reference guide - https://lnkd.in/dGjp2MNC

✅SQL on Big Data - Technology, Architecture, and Innovation: https://lnkd.in/dyhahYqh

✅edX  Linux - https://lnkd.in/dSRRcST5

✅Medium blogs - https://lnkd.in/dSjTF2yr

✅30 days of Data Engineering by @Naina Chaturvedi - https://lnkd.in/dcSwYtxT

✅Spark - https://lnkd.in/di87FHcU

✅Kafka - https://lnkd.in/dVm2xPfY

✅Amazon Web Services (AWS)  - https://lnkd.in/dZqVrV3S

✅Data Engineering with Google Cloud - https://lnkd.in/dx9FPHjC

🌀Whereas there might still be few skills that are part of day to day data engineer's activities such as ➖

🔺Strong problem-solving and debugging skills

🔺Experience with version control systems such as Git

📝Hope this post helps you understand how to begin with this informative "𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝟐𝟎𝟐𝟑 𝐑𝐨𝐚𝐝𝐦𝐚𝐩" and accordingly create your own journey in the field of data!! 

✔️CREDITS: Alexandra Abbas

#dataengineering

❌Gathering all the data as it is from source is never of use! 

✔️Whereas having the data processed, transformed and loaded into various categories makes sense!! 

👉It's like - “No one ever made a decision because of a number. They need a story.” - Daniel Kahneman

🌀But, it doesn't mean that numbers/ data aren't valuable. 

⏸️They are valuable, but only if the right information and insightful values are derived to make decisions. 

🛵Whereas, storytelling is an important part for every data enthusiast to keep on learning, growing, experiencing and taking decisions. 

🎯𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 has an important concept of data lake, data warehouse and data marts one must understand before deep diving.

♐️A 𝐝𝐚𝐭𝐚 𝐥𝐚𝐤𝐞 is a raw data repository that allows you to store all your structured and unstructured data at any scale. 
It is typically used for storing and processing large volumes of raw data. This means that data lakes can be used to store a wide range of data types, including structured data, semi-structured data (such as JSON or XML), and unstructured data (such as log files or social media posts).
Data lakes are particularly useful for storing large volumes of data, as they allow you to store and access data without the need to pre-process or transform it. 


♐A 𝐝𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 is a centralised repository for storing structured data, typically from a variety of sources. 
The data in a data warehouse is typically transformed and cleaned up to be more useful for analysis, and is stored in a way that is optimised for fast querying and analysis using SQL and other query languages.
Data warehouses are often used by organisations to support business intelligence and analytics initiatives, as they allow analysts and data scientists to easily access and query large datasets in order to gain insights and make data-driven decisions. 

♐A 𝐝𝐚𝐭𝐚 𝐦𝐚𝐫𝐭 is a smaller, more focused version of a data warehouse that is designed to support the decision-making needs of a specific department or business unit. 
Data marts are often used to support real-time analysis and decision-making, as they contain a more limited set of data that is more relevant to the specific needs of the business unit or department. 
 
🤔 Does this sound relatable and easy to understand? 

🔖It's isn't that easy to implement or actually get these information, lots of tools, technologies and business understanding is required while traversing through these phases to get the right set of data for end users!! 

♾️Understand more about the Extraction Transformation and Loading concepts to know how each one exhibits data and what kind of processing is required to consume and utilize data. 

🔰Image Credits: Marc Lamberti 

#data #storytelling #learning #business #warehouse #bigdata #cloudcomputing #dataengineering #datamining #computing #analytics

🔰𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝟑.𝟎 𝐐𝐮𝐢𝐜𝐤 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐆𝐮𝐢𝐝𝐞!! 

♐Why Apache Spark❓

📍Apache Spark is an open-source distributed computing system designed for large-scale data processing with efficient speed and general-purpose engine for large-scale data. 

📍Effectively designed to be highly scalable, and allows to process large amounts of data very quickly. It is often used for big data analytics, machine learning, and real-time stream processing.

📍Spark has a number of features that make it well-suited for big data processing, including:

✅In-memory processing: Spark stores data in memory, which makes it much faster than traditional disk-based systems.

✅Resilient Distributed Datasets (RDDs): Spark uses RDDs to distribute data across a cluster of computers, which makes it easy to parallelize data processing tasks.

✅Efficient execution: Spark has a number of optimization techniques that make it efficient at processing large datasets, such as pipelining and data compression.

✅It can support a wide range of data sources: can read data from a variety of sources, including HDFS, HBase, Cassandra, and more.

✅Multiple APIs: Spark offers APIs in Scala, Python, R, and SQL, making it easy to use with a wide range of data processing tasks.

➡️Why is Apache Spark an important framework for Data Engineers? 

🎯Apache Spark is often used for data engineering tasks, such as:

🌀Extracting data from various sources, such as databases, log files, and APIs.

🌀Transforming data by cleaning it, aggregating it, or modifying it in some way.

🌀Loading data into a data store, such as a database or data warehouse, for further analysis or reporting.

🌀Building data pipelines to automate the process of extracting, transforming, and loading data.

🔖Looking to learn PySpark with interesting hands on use cases, refer to the amazing cheat sheet that has a variety of quick operations list that Spark can perform in no time. 👇

♾️Get your hands dirty with SparkbyExamples an amazing reference with interesting examples to explore - https://lnkd.in/di87FHcU

#data #dataengineering #dataanalytics #bigdata #machinelearning #sql #python #computing #intelligence #engineering #analytics #cloud #growth #database 

♐️Where are we heading on to with the changing times?

👉Is it ChatGTP?

🎯With the evolving times from 1993 till 2022 the change in the Most used websites have evolved!

▶️There's been a significant growth with is quite editable from this interesting video sharing insights to understand various websites been used with the growth of internet.

📊Now Google being the top player, what's your though, will this change in the coming times?

📍Share your thoughts in the comments section!!👇

#data #dataengineering #growth #dataanalytics #businessintelligence #datavisualization #artificialintelligence #chatgpt #google 

❌10 things not to do as a data engineer from my experience -

♐️Don’t forget to backup your data - avoid data loss
♐️Don’t underestimate the skill to communicate - have clear communication within the team and stakeholders
♐️Don’t ignore the power of testing - to test pipeline execution at early stage and avoid rework
♐️Don’t ignore scalability - Ensure data pipelines are scalable enough to handle load
♐️Don’t neglect data quality - have consistent, accurate and complete data.
♐️Don’t underrate the power of documentation - It helps to understand process within the team for better maintenance
♐️Don’t overlook the security aspects - to have enough accesses and authorisation, have with secure data avoid unauthorized access or breach
♐️Don’t disregard data governance - follow best practices to ensure compliance
♐️Don’t overlook the importance of monitoring and maintenance - to track performance issues and better traceability
♐️Don’t stick to limited technologies - keep up-skilling and stay updated with latest technologies in the field

👉There are a lot other best practices, may be other factors that must be kept in mind while working as a data engineer..

🧭I would like to know what not to do as a data engineer from your experience.. Share in comments!👇

#data #engineering #cloud #analytics #growth #bigdata #dataengineering #dataanalytics #googlecloud #computing #learning #security #communication #scalability 

😉 Those days of pen drives and hard drives have gone!! 

♐Which cloud service provider remains to top the market❓

🔰Cloud market is being heavily dominated by great giants Amazon Web Services (AWS) , Google Cloud & Microsoft Cloud !! 

♾️Each one of it has its own pros and cons, where it's difficult to compare. 

📍The majority of top cloud service providers offer all services such as SaaS, PaaS & IaaS. The two leaders in the cloud computing world are Amazon and Microsoft, followed by Google, Alibaba, and IBM. 

📊Whereas the chart here clearly states the winner to be AWS, due to it's popular choice among data engineers having comprehensive set of services and tools for data storage, processing, and analytics.
 
🎯Some of the most commonly used services by data engineers on AWS include S3 for storage, Redshift for data warehousing, and EMR for big data processing. 

✔️AWS also offers a wide range of tools for data visualization, machine learning, and IoT, which makes it a popular choice for data engineers. 

🌀Other popular cloud service providers for data engineers include Microsoft Azure and Google Cloud Platform (GCP). 

👉Both Azure and GCP offer similar services and tools as AWS, but they have different strengths and use cases.

✅AWS is the most popular and widely adopted cloud provider, and it offers a wide range of services and tools, making it a great option for data engineers. AWS is a leader in the cloud market, and it's expected to continue to grow in the future.

🔖Image Credits : Statista & Synergy Research Group

🥸Let’s look at some customers for each platform.

🔹Google Cloud platform is home to gaming company Activision Blizzard, Airline Lufthansa, and SpaceX.

🔹Azure boosts mega customers, such as AT&T, Coca-Cola, and Walmart. (The latter has even announced some special partnerships with Microsoft, presumably joining forces to compete with Amazon.)

🔹AWS counts many huge companies as customers. Netflix is probably the most well-known given their massive streaming infrastructure. Online companies like Twitch and Twitter also use the platform, as do media companies like Turner Broadcasting and the BBC.

🙌Thanks for sharing this informative customer list Scott Pletcher 

#cloud #data #google #aws #amazon #microsoft #azure #bigdata #analytics #cloudcomputing #growth #leaders 

♐𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬𝐞𝐭 𝐢𝐧 𝟐𝟎𝟐𝟑!! 

➡️A data engineer is responsible for designing, building, maintaining, and troubleshooting data pipelines. 

🎯These pipelines extract data from various sources, transform the data as needed, and load it into a central data store or data warehouse for analysis and reporting.

✅It is difficult to predict with certainty which specific skills will be most in-demand for data engineers in 2023, as the field is constantly evolving. 

🔰However, some skills that are likely to remain important for data engineers in the coming years include:

🔖𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬: Data engineers will continue to need strong programming skills in languages such as Python, Java, or C++. These skills are necessary for building and maintaining data pipelines and working with big data technologies.

🔖𝐒𝐐𝐋 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐬𝐤𝐢𝐥𝐥𝐬: Data engineers should be proficient in SQL and have a strong understanding of database design and data modeling concepts.

🔖𝐃𝐚𝐭𝐚 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐚𝐧𝐝 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬: Knowledge of technologies such as Hadoop, Spark, and Apache Flink will be important for data engineers to efficiently store and process large amounts of data.

🔖𝐂𝐥𝐨𝐮𝐝 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬: Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform will be valuable for data engineers as more organizations move their data and processing to the cloud.

🔖𝐃𝐚𝐭𝐚 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬: Data engineers should have a basic understanding of data visualization and reporting tools such as Tableau or Power BI, as they may be responsible for creating reports and dashboards for stakeholders.

🔖𝐃𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐬𝐤𝐢𝐥𝐥𝐬: As data becomes increasingly important for organizations, data engineers will need to ensure that data is secure and compliant with relevant regulations and policies.

🔖𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐬𝐤𝐢𝐥𝐥𝐬: Data engineers will need to work effectively in teams and communicate with data scientists, business analysts, and other stakeholders.

♾️Whereas there might still be few skills that are part of day to day data engineer's activities such as -

➖Strong problem-solving and debugging skills

➖Experience with version control systems such as Git

📊Share your opinion on what other important skills must a data engineer pursue. 👇

🥳LinkedIn Wish you all a very Happy and successful new year 🎉🎇

#data #dataengineering #bigdata #cloudcomputing #business #programming #science #engineering 

🔰Azure Databricks is a fully managed cloud service that provides a platform for data engineering, data science, and data analytics.

➡️It is designed to enable secure collaboration among data scientists, data engineers, and other data professionals, allowing them to work together on data projects in a unified workspace. Azure Databricks integrates with a number of Azure services, such as Azure Synapse, Azure Data Lake Storage, and Azure SQL Database, making it easy to build and deploy data pipelines, analyze data, and build machine learning models. 

🔖It also includes a variety of tools and libraries for working with data, including Apache Spark, Delta Lake, and MLflow, as well as integration with Azure DevOps and GitHub for version control and collaboration.

♾️Microsoft Azure Databricks is a fully managed version of the open-source Apache Spark platform, which is widely used for big data processing and data engineering. This allows teams to leverage the full power of Azure's cloud computing and data management capabilities while using Azure Databricks.

♐Azure Databricks provides a range of features to support team collaboration, including integration with Azure Active Directory for secure user authentication and access control, as well as version control and data lineage tracking to help teams track changes to data and code. It also includes tools for data exploration, visualization, and machine learning, allowing data scientists to easily build and deploy models.

👉Here's an insightful and well curated resource that comprises various informative architectures from Azure Databricks !! 

#microsoft #azure #databricks #dataengineering #dataanalytics #datascience #machinelearning #bigdata #cloud #data #devops #cloudcomputing #github #projects #analytics #engineering #learning 

🎭Data has many faces even when it’s not doing engineering 😉

➡️𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 - It refers to the simulation of human intelligence in machines that are programmed to think and act like humans. 
🔖These intelligent machines can be trained to perform a variety of tasks, from simple to complex, by processing and analyzing large amounts of data and making decisions based on that information.
♐However, the development and implementation of AI also raise ethical concerns and social issues, such as the potential for job displacement and the need to ensure the systems are transparent and accountable.

➡️𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 - ML is a subset of artificial intelligence (AI) that involves the development of algorithms that can learn from and make predictions or decisions based on data. ML algorithms are trained on a dataset and can improve their performance over time by adapting to new data and experiences.
🔖There are several types of machine learning, including supervised , in which the algorithm is trained on labeled data and makes predictions based on that training, and unsupervised, in which the algorithm is not given labeled data and must find patterns and relationships in the data on its own. 
♐It has the potential to significantly improve the efficiency and accuracy of many tasks, but it also raises ethical concerns related to the potential for biased or unfair decisions made by machine learning algorithms.

➡️𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 - A subfield of machine learning that is inspired by the structure and function of the brain, specifically the neural networks that make up the brain.
🔖It involves the use of neural networks, which are complex mathematical models inspired by the structure and function of the human brain. It requires a large amount of labeled data and significant computational power, but it has the potential to achieve state-of-the-art results in many tasks.
♐Deep learning is particularly well-suited to tasks that involve analyzing and interpreting complex data, such as image and video, text, and audio data.

➡️𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 - A field that involves the use of scientific and mathematical methods, algorithms, and systems to extract insights from structured and unstructured data. A wide range of activities encompasses - data collection, processing, analysis, visualization, and communication.
🔖They work with large and complex datasets, and use statistical and machine learning techniques to identify patterns and trends, and to make predictions or decisions based on that data.
♐It plays a critical role in helping organizations to make data-driven decisions, and to understand and improve their operations and customer experiences.

🎯Here's an insightful carousel by Andrew Jones from DATA SCIENCE INFINITY that well describes the informative references to these areas where data is impactful and plays a significant role. 

#artificialintelligence #machinelearning #deeplearning #datascience

♐𝟗𝟕 𝐓𝐡𝐢𝐧𝐠𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰! 

🕶Get collective wisdom curated by industry experts!! 

👉It begins from, 

❗Data storage to data science, 
▪️Infrastructure to microservices, 
❗Data security to data quality, 
▪️Databases to programming, 
❗CAP Theorem to data lake, 
▪️Automation to sensors, 
❗Data capturing to pipeline building, 
▪️ETL/ELT to batch/streaming process, 
❗Distrubuted programming to cloud computing, 
▪️Tardy data to big data, 
❗Data scarcity to synthesis data, 
▪️Data visualization to dashboards, 
❗Business understanding to storytelling.. 

...and has a lot many features and details a data engineer must know!! 🤓

♾️We've been talking about it, learning about it, experimenting about it, but these skills help you sustain grow, learn-unlearn, the important aspects of data engineering. 

🎯Here's an amazing resource by O'Reilly Media Curated by : Tobias Macey. This guide really helps you understand a lot many do's and don'ts by industry experts who have been amazing mentors to guide us. 

🏷Some contributors of this book are from Twitter Google Capital One Microsoft Stitch Fix and LinkedIn, you know these companies have been working with data so long and engineering them to provide amazing solutions. 

📍This book is a gem who's looking forward to build a career in data engineer or someone who is pursuing it. 

➖Data Engineering is not a nut shell of data pipelines or technical advancements. It’s about understanding the data flow and aspects to understand concepts, technology implications.

🔰A data engineer is responsible for designing, building, maintaining, and troubleshooting data pipelines.

🎯It is difficult to predict with certainty which specific skills will be most in-demand for data engineers in 2023, as the field is constantly evolving. 

🌀However, some skills that are likely to remain important for data engineers in the coming years include:

🔖 Programming 
🔖 SQL and database skills
🔖 Data Storage and Processing
🔖 Cloud Computing
🔖 Data Visualization and Reporting
🔖 Data Governance and Security
🔖 Collaboration and Communication
🔖 Strong problem solving and debugging skills
🔖 Version Control and Git

📝With enormous amount of resources available widely I’m sharing few important resources, one can refer -

✅Data Engineering with Python by Packt - https://lnkd.in/dP9hVDcB
✅Python data cleaning reference guide - https://lnkd.in/dGjp2MNC
✅SQL with Ankit Bansal - https://lnkd.in/dAPgKtYZ
✅SQL on Big Data - Technology, Architecture, and Innovation - https://lnkd.in/dyhahYqh
✅Spark - https://lnkd.in/di87FHcU
✅Kafka - https://lnkd.in/dVm2xPfY
✅Amazon Web Services (AWS) - https://lnkd.in/dZqVrV3S
✅Data Engineering with Google Cloud - https://lnkd.in/dx9FPHjC

#data #engineering #analytics #sql #nosql #dataengineering #cloud #python #computing #innovation #bigdata #programming #datascience #datavisualization #cloudcomputing #aws #gcp #azure #dataanalytics #intelligence  

▶️How to ensure you are data driven❓

👉Here’s an insightful graphics that depicts what does data driven mean and how can we ensure we’re data driven!

💯Data isn't limited for files, logs, tables, it is endless. It is going to keep on growing with time be it through social media, internet, e-commerce sites or other industrial sources.

🔰Being data-centric means that an organization or system places a strong emphasis on utilizing data as the primary means of making decisions and driving actions. 

💠This often involves the collection, analysis, and interpretation of large amounts of data in order to inform strategy, identify trends, and optimize processes. 

➤Data-centric organizations will often invest heavily in data management and analytics tools in order to make the most of the data they collect.

🎯There are several ways to ensure that an organization is data-driven:

♐️Collect and store data 
🧭Encourage data-driven experiments and experimentation culture.
♐️Communicate the data-driven insights 
🧭Develop a data culture
♐️Use data to make informed decisions
🧭Continuously measure and improve decision making
♐️Invest in data analytics tools
🧭Establish data governance
♐️Encourage data-driven experimentation and experimentation culture.
🧭Communicate the data-driven insights to stakeholders and decision makers

📌Infographic Credits: Amazon Web Services (AWS)

#data #dataanalytics #aws #bigdata #dataengineering #datascience #socialmedia  #datamanagement #analytics #amazon #ai #ml #engineering #cloudcomputing

➡️What's the most preferred skill to have as a data engineer? 
🔰Sometimes it's not about the tools but about the you're working on! 

💯My experience shoutouts mostly to understand the data warehouse and etl concepts, understand the processing mechanism instead of tools.. 

📊Whereas having visual representation of facts and figures, volume and data categories is utmost important part of a data professional, be it working into any domain and any job profile. 

📍What's your preferred visualization tool you work mostly with? 

♐Tableau
♐PowerBI
♐Looker
♐Others(share in comments) 💬

👉Why not share your polls across to understand the demand of visualization tool.

 #data #tableau #powerbi #looker #datavisualization

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