Globant

Senior Data Engineer

LinkedIn

LinkedIn Learning Instructor

CRISIL Limited

Senior Analyst - Data Engineer

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 | Senior Data Engineer@ Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022 | Ex- CRISIL, QUANTIPHI, TCS

Data Engineer with 5 years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

I am pursuing a Master of Technology in Data Science and Engineering at Birla Institute of Technology and Science, Pilani, where I learn advanced concepts and techniques in data science, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as a Data Science and Women Empowerment Thought Leader by Global AI Hub. I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

REST API Vs. GraphQL 
. 
. 
When it comes to API design, REST and GraphQL each have their own strengths and weaknesses.  
  
REST  
- Uses standard HTTP methods like GET, POST, PUT, DELETE for CRUD operations.  
- Works well when you need simple, uniform interfaces between separate services/applications.  
- Caching strategies are straightforward to implement.  
- The downside is it may require multiple roundtrips to assemble related data from separate endpoints.  
  
GraphQL  
- Provides a single endpoint for clients to query for precisely the data they need.  
- Clients specify the exact fields required in nested queries, and the server returns optimized payloads containing just those fields.  
- Supports Mutations for modifying data and Subscriptions for real-time notifications.  
- Great for aggregating data from multiple sources and works well with rapidly evolving frontend requirements.  
- However, it shifts complexity to the client side and can allow abusive queries if not properly safeguarded  
- Caching strategies can be more complicated than REST.  
  
The best choice between REST and GraphQL depends on the specific requirements of the application and development team. GraphQL is a good fit for complex or frequently changing frontend needs, while REST suits applications where simple and consistent contracts are preferred.  
  
–  
Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/3KCnWXq  
  
#systemdesign #coding #interviewtips  
.

POOJA JAIN

This approach views success as a shared journey where everyone has the opportunity to thrive.

It also acknowledges that someone else's accomplishment does not take away from your own.

The greatest leaders not only strive to reach their own goals but also  genuinely want to see others succeed, too.

Gary Travis #garytravis #careers #culture #leadership #leadershipdevelopment

Back from the Data Cave: Cloud Data Pipelines❗️

Hello LinkedIn data fam!   I've been lately away from LinkedIn for a while, but the data world never sleeps, right? 

Let's dive into a topic that I've been lately exploring deeper - cloud data pipelines! ☁️

It is always important to brushup the concepts and here's me comparing the big three: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud platforms.  

Each of them offers a unique toolkit for building data pipelines, from data ingestion all the way to sweet visualizations.  

Here's a quick sneak peek:
♐️Data Ingestion:
    Azure: Ever used Data Factory? Streamlining data collection is its jam.
    AWS: Data Pipeline and Kinesis - built for scalable data influx.
    GCP: Dataflow and Pub/Sub - real-time streaming, anyone?

♐️Data Lakes:
    Azure: Azure Data Lake Storage keeps things organized with hierarchical namespaces.
    AWS: Lake Formation - managing your data lake just got easier.
    GCP: BigQuery Omni - analyze data across clouds, no sweat!

♐️Processing:
    Azure: Azure Databricks - processing on steroids! ⚡
    AWS: Glue - effortless data prep and transformation.
    GCP: Dataprep by Trifacta - user-friendly magic for your data.

♐️Data Warehousing:
    Azure: Synapse Analytics - warehousing and analytics, all in one.
    AWS: Redshift - efficient analysis for massive datasets.
    GCP: BigQuery - serverless, scalable, what more could you ask for?

♐️Presentation Layer:
    Azure: Power BI - transform data into actionable insights with stunning visuals.
    AWS: QuickSight - democratizing insights with ML-powered dashboards.
    GCP: Data Studio - easy-to-use reporting that turns data into stories.

🤔Now comes the big question:  With so many amazing options, how do you choose the right platform for your needs?

According to you what are some key considerations when selecting a cloud data pipeline solution?  
👉Budget, tech stack, specific functionalities❓ 

Let's share our experiences and help each other navigate this ever-evolving data landscape!

Big thanks to Satish Chandra Gupta and Rocky Bhatia for sharing this amazing infographics.

#data #engineering #aws #azure #gcp #cloud

👉The "Data Engineering Dictionary: 2024 Edition" (for the Busy Engineer) with wide range of responsibilities related to designing, building, and maintaining data infrastructure and pipelines.

🕵️‍♂️Imagine you're a detective looking for clues. Data is like a giant pile of evidence, and these terms are your tools to make sense of it all!

📍Data Mining: Uncovering secrets in data like a detective finds hidden patterns using fancy data minings tools to extract data and connections within data.
📍Data Analytics: Examining data to answer questions and make smart decisions. Think of it like analyzing clues to understand what happened.
📍Data Visualization: Turning complex data into charts and graphs, like a detective's sketch of the crime scene, making it easier to explain what you found.
📍Data Contract: An agreement between data sharers, like a detective making sure they both understand the evidence (data) they're exchanging.
📍Data Modeling: Building a blueprint for your data, like a detective sketching the layout of a crime scene, to organize everything clearly.
📍Data Integration: Combining data from different places, like integrating together clues from witnesses, to get a complete picture.
📍Data Cleaning: Fixing mistakes in the data, like a detective checking if a witness saw correctly, to make sure your analysis is accurate.
📍Data Warehouse: A central storage for organized data, like a detective's evidence room, where everything is kept safe and accessible for further investigation.
📍Data Mart: A smaller, focused data storage for a specific department, like a detective focusing on fingerprints for a case.
📍Data Lake: A big storage for any kind of data, like a detective's messy desk with notes, photos, and videos, all waiting to be reviewed.
📍Delta Lake: A more reliable data lake, like a detective keeping track of changes to evidence with timestamps, ensuring nothing gets lost.
📍Data Pipeline: A moving assembly line for data, transforming it from one format to another, like sending evidence to a lab for analysis.
📍Data Mesh: Spreading data ownership across teams, like detectives specializing in fingerprints or ballistics, each responsible for their area.
📍Data Lake House: A blend of data lake and warehouse, like a detective's workspace with both organized files and a big box of miscellaneous evidence.
📍Data Swamp: A messy data graveyard, like a pile of unorganized evidence, where it's impossible to find anything useful.
📍Data Fabric: A unified view of all data sources, like a detective board with connections between all the clues, making it easy to see the bigger picture.

Understanding these terms will help you navigate the exciting world of data. Mastering these concepts and focusing on each terminology can help professionals  provide business-critical data infrastructure and power up the applications!

What else would you like to my dear #data #engineering enthusiasts❓

♐️Image Credits: Brij kishore Pandey

#dataengineering

👉 Apache Spark for data engineers is like SQL is for relational databases. 

♐️Just as SQL is a standard language used to interact with and manipulate data in relational databases, Apache Spark provides a powerful framework for processing and analyzing data in a distributed computing environment. 

👩‍💻With Apache Spark, data engineers can perform complex data transformations, machine learning tasks, and data analysis on large-scale datasets in a scalable and efficient manner. 

📌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.

✅ 𝐒𝐮𝐩𝐩𝐨𝐫𝐭𝐬 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.

Sharing few insightful and well created resources to learn spark for free -

📝Here's a set of insightful resources to learn Spark:

- Get started with Apache Spark - https://lnkd.in/d8bqkiGa
- Spark Starter Kit free course on Udemy - https://lnkd.in/gdSSWmws
- PySpark with Krish Naik - https://lnkd.in/dNqwptBA
- Get your hands dirty with SparkByExamples an amazing reference with interesting examples to explore - https://lnkd.in/di87FHcU
- Apache Spark tutorial by Databricks - https://lnkd.in/gaUZqNm5
- Explore PySpark projects with Alex Ioannides - https://lnkd.in/dxhYZMJG
- Learn to tune and optimize Spark Jobs - https://lnkd.in/dA5yPmgG
- Build game-changing data-driven apps by integrating MongoDB and PySpark by Aashay Patil - http://bit.ly/42iM2xC
- Prepare for interviews with amazing Apache spark reference - https://lnkd.in/dwb4CDjr
- Hands-on Apache Spark using Python with Wenqiang Feng, Ph.D. on GitHub - https://lnkd.in/d2X9ecJQ

🔖Here's a well-curated resource I'm sharing that explains how to create Pyspark snippets that are equivalent to SQL queries curated by Shwetank Singh !!

#bigdata #engineering #dataanalytics #data #python #spark #dataengineering #sql #analytics #pyspark #datamining

SQL and PySpark

Data is the fuel, but turning is into knowledge and action is through collaboration.

🕵️‍♂️ engineers build the pipelines, data scientists unlock the secrets, and machine learning engineers turn insights into action. Together, they transform data into a powerful competitive advantage.

It involves collaboration between various data teams to leverage the power of turning data into actionable solutions driving outcomes that drives businesses.

>> It is important for a data team to work together collaboratively for several reasons:

- Diverse Skill Sets 
- Data Quality 
- Innovation 
- Better Communication 
- Better decision-making 
- Improved Efficiency 
- Stronger team relationships

Precisely focusing on the data engineering lifecycle, following depicts various steps in preparing data for analysis and decision-making:

🔄 Data Ingestion: Efficiently importing data from various sources.
✅ Data Validation: Ensuring data quality and accuracy.
🧹 Data Cleansing: Correcting or removing corrupt records.
📐 Data Standardisation: Unifying data formats for ease of analysis.
🗂️ Data Curation: Managing data throughout its lifecycle.

👉In order to deliver high-quality data products, each member of the team needs to communicate effectively with each other, share their expertise and knowledge, and work collaboratively towards a common goal. 

📌Data quality is a collective responsibility that requires all team members to do their part in ensuring that the data is accurate, up-to-date, and suitable for the task at hand.

Image Credits: Deepak Bhardwaj

#data #ml #bigdata #dataengineering #analytics #science #engineering #dataanalytics #datascience #machinelearning #innovation

Interdependent or independent❓

♐️How do you see these 4 roles of data - Data Engineer/ Data Scientist/ ML Engineer/ Data Analyst?

➡️How do various data professionals collaborate to ensure they drive the overall data and analytics team?

The answer isn't that they compete with each other instead, each data professional complements the other in the following manner -

📍Data Engineers - "Lead the Way" by providing the infrastructure and data pipelines that data analysts, data scientists, and ML engineers rely on. 

📍 Data Analysts - "Provide Direction" by share insights and requirements that help guide the work of data scientists and ML engineers. 

📍 Data Scientists and ML Engineers - "Unlocking Hidden Patterns" to collaborate closely to develop and deploy machine learning models while relying on data engineers to access and prepare the necessary data. 

Whereas, we all know at each stage from data gathering model prediction into production to taking business intelligence, the entire data team is the backbone. 

Yet we try to differentiate and exaggerate the fact that "various data roles are competing in the market", whereas all of these complement each other well in advance to meet the targets and drive business outcomes.

👉Here's an amazing illustration specifying the key roles of each data profession by Kevin Rosamont Prombo.

As per my experience, sharing my opinion on data professional's collaboration -

♐️ Understanding data sources is crucial for data scientists. They work with data engineers to ensure they have the necessary data for analysis and modeling.
♐️ Optimizing data pipelines is essential for efficient machine learning. Data engineers collaborate with data scientists to achieve this.
♐️ Data scientists play a key role in defining data requirements. They provide guidance and specifications to data engineers regarding the types of data and features needed for their work.

Open to hearing your views about the diversity in the data profession competing against each other instead of augmenting together. 

#data #machinelearning #analytics #engineering #science #bigdata #businessintelligence #datascience #dataanalytics #dataengineering

Apache Spark on Cloud is one of the most trending skills to have and utilizing managed Spark services on the cloud is often a convenient and efficient option.

📍Here’s how you can utilise Spark on Google Cloud:
- Dataproc
- AI Platform Notebooks
- Data Flow, Data Prep
- Compute Engine

♐️When using Spark on GCP, you can leverage other GCP services for data storage, processing, and analytics. For example, you can store your data in Google Cloud Storage (GCS), process it with Spark, and use BigQuery for data warehousing and analytics.

📍Here's a high-level overview of how you can utilize Spark on Amazon Web Services (AWS):
- Amazon EMR
- Amazon EC2
- AWS Glue,  Kinesis Analytics
- AWS Batch

♐️When using Spark on AWS, you can leverage other AWS services for data storage, processing, and analytics. For example, you can store your data in Amazon S3, process it with Spark, and use services like Amazon Athena, Amazon Redshift, or Amazon Quick Sight for data analytics and visualization.

📍Overview of how you can utilize Spark on Microsoft Cloud Microsoft Azure:
- Azure Databricks
- Azure Synapse Analytics
- Azure Hd Insight
- Azure Machine Learning

♐️When using Spark on Azure, you can leverage other Azure services for data storage, processing, and analytics. For example, you can store your data in Azure Data Lake Storage, process it with Spark, and use services like Azure SQL Database, Azure Synapse Analytics, or Power BI for data analytics and visualization.

🔰Beginners can focus below mentioned spark components - 
- Ease of Use
- Scalability
- Data Sources and Formats
- Integration with Other Cloud Services
- Integration with the Big Data Ecosystem
- Managed Security

🔰Professionals focus on the following Spark specifications - 
- Advanced Configuration
- Cost Optimization
- Advanced Workflows
- Data Partitioning and Shuffling
- Integration with Data Warehouses and Data Lakes
- Real-time Stream Processing

Overall, managed Spark services on the cloud provide a great starting point for beginners to learn and experiment with Spark while offering professionals a convenient and scalable solution. 

As you gain more expertise, you can explore advanced configurations, cost optimization, and workflow management to maximise the potential of Spark on the cloud.

👉Here's an amazing infographic by Satish Chandra Gupta depicting the Big Data Pipelines by different cloud service providers. 

#dataengineering #analytics #aws #gcp #azure #bigdata #cloudcomputing #datamining #engineering #dataanalytics #ai #ml #data

Planning to prepare for data engineering opportunities❓

Sharing few informative Youtube references to prepare for DE Interviews:

♐️ Andreas Kretz - https://lnkd.in/gbkgbmMG
♐️ Darshil Parmar - https://lnkd.in/gGAu-9qV
♐️ Sumit Mittal - https://lnkd.in/ggyHfxgJ
♐️ Gowtham SB - https://lnkd.in/gRecFDnE
♐️ Seattle Data Guy - https://lnkd.in/gQ5Dpby9
♐️Sandip Das - https://lnkd.in/gkNPkGys
♐️ Data Engineering Simplified - https://lnkd.in/gsVRwQ8c

Data Professionals can also explore few of these- 
📍 Alex Freberg - https://lnkd.in/gg77b_iH
📍 Ken Jee - https://lnkd.in/gcBjVimJ
📍 Ankit Bansal - https://lnkd.in/gPpWQTx2

Here's a list of github repositories to explore for data engineers -

 ➡️ DataTalksClub - https://lnkd.in/gKx7yUtm
➡️ DataExpert.io by Zach Wilson - https://lnkd.in/gffKBq_A
➡️ Data Engineering Cookbook by Andreas - https://lnkd.in/g_3xqMaE

These are just a few resources that can help you leverage the most of data engineering challenges.

Did I miss something? Please feel free to share in the comments 👇

#data #engineering

AWS Cloud Services is a vast landscape to explore for data engineers.

Serverless services provided by various cloud providers, here's a small list:
📍Lambda
📍Elastic Beanstalk, 
📍ECS/EKS
📍Fargate

Database provided by AWS includes:
- Amazon DynamoDB
- Amazon ElastiCache
- Amazon Kinesis
- Amazon Redshift 
- Amazon SimpleDB

Here's a high-level overview of how you can utilize Spark on Amazon Web Services (AWS):
🔰 Amazon EMR
🔰 Amazon EC2
🔰 AWS Glue, Kinesis Analytics
🔰 AWS Batch

♐️When using Spark on AWS, you can leverage other AWS services for data storage, processing, and analytics. For example, you can store your data in Amazon S3, process it with Spark, and use services like Amazon Athena, Amazon Redshift, or Amazon Quick Sight for data analytics and visualization.

➡️ AWS journey for a data engineer is a continuous learning process. Precisely focusing on the core services and adopting a strategic learning approach, can help you leverage the power of AWS to build robust, scalable, and efficient data-driven solutions.

Some additional specific data engineer insights on AWS services:
✅Cost Optimization: Services like AWS Spot Instances and EC2 Reserved Instances offer cost-effective alternatives for compute resources when possible.
✅Security & Compliance: Data engineers play a crucial role in ensuring data security and compliance on AWS. Utilize features like AWS IAM for access control and encryption services like KMS to safeguard sensitive data.
✅Automation & DevOps: Leverage tools like AWS CloudFormation and AWS CodePipeline to automate infrastructure provisioning and data pipeline deployments, streamlining your workflow.

🎉Looking to explore more then here are few folks you can follow to stay updated with different cloud service providers:

- Google Cloud : Priyanka Vergadia, Cassie Kozyrkov
- Amazon Web Services (AWS): Viktoria Semaan, Stéphane Maarek, Neal Davis
- Microsoft Cloud #Azure: Deepak Goyal, Chris Pietschmann, Magnus Mårtensson ☁

Here's an interesting infographics curated by Alex Xu(ByteByteGo) to understand the important AWS services to learn.

#data #engineering #cloud #business #Innovation #growth

"Data problems cannot be solved by an individual It's a team game! It's about collective effort."

👩‍💻In the world of big data, it's not about hoarding tasks or responsibilities. 

It doesn't matter whether you're an ML Engineer, Data Engineer, DevOps Engineer, Cloud Engineer or a Manager. Everybody has a crucial role in a data project. 

Collaborative work can elevate results, enhance efficiency, and foster a more positive work environment. 

Why is it vital for a data team to collaborate❓
- Diverse Skill Sets 
- Data Quality 
- Innovation 
- Improved Communication 
- Enhanced decision-making 
- Boosted Efficiency 
- Stronger team relationships

Let's delve into how each member of the data team relies on the others: 

𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 🛠️: Design, build, and maintain the data infrastructure. They work with ML engineers and data scientists to ensure high-quality data for modeling.

𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 🖥️: Tasked with building and deploying scalable, efficient machine learning models. They collaborate with data engineers to retrieve the necessary data.

𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭𝐬 📉: Analyze data, build statistical models, extract insights, make predictions, and work closely with data engineers to guarantee high-quality, suitable data.

𝐃𝐞𝐯𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 🔧: Build, deploy, and maintain infrastructure. They work with all team members to ensure reliability and scalability.

𝐌𝐚𝐧𝐚𝐠𝐞𝐫𝐬 🎯: Oversee the team's work, ensure project delivery on time and on budget. They understand project requirements, identify risks, and make crucial decisions for success.

To deliver high-quality data products, team members need to communicate effectively, share their expertise, and collaborate towards a common goal. 

Remember, data quality is a collective responsibility. Everyone must ensure the data is accurate, up-to-date, and suitable.

By leveraging each other's strengths, data teams can produce superior results, work more efficiently and create a more positive work environment. 

👉Few links and resources to prepare yourself for data engineering -

♐️Python for data engineering - https://lnkd.in/g6A6jJ7j
♐️SQL for data engineering -https://lnkd.in/gv3cYYqf
♐️Cloud for data engineering - https://lnkd.in/gjnRy3bf
♐️System Design by Alex Xu - https://lnkd.in/gwm9-H7a
♐️Data Warehouse concepts are important for bigdata engineering - https://lnkd.in/gzWPXj-D
♐️Building a data lake house with Google Cloud -
https://lnkd.in/geMjfxut
♐️Apache Spark for data engineers - https://lnkd.in/gpjtVTuz
♐️Amazon Web Services (AWS)  Google Cloud Microsoft Cloud database services - https://lnkd.in/g2MUqDKD

#data #analytics #engineering #bigdata #dataengineering #cloudcomputing #dataanalytics #datascience #machinelearning #artificialintelligence

Is data engineering analytics: An ultimate skill blend❓

🧑🏻‍💻With enormous opportunity out there for data professionals, I see data engineering analytics as one of the rising demands, with skills of both data engineers and analysts accumulated together.

📍As a data engineer with experience in analytics, I'm curious to hear your thoughts! 
What skills have been most valuable in your data journey? 

👉From my perspective, 3 key areas where these skillsets converge:

➡️Data Wrangling Wizardry 🪄 & Cloud Proficiency ☁️: Taming messy data is an art, and cloud platforms like AWS, Azure, and GCP provide the tools to sculpt it.  Can you wrangle data while leveraging the cloud's scalability?

➡️Programming Powerhouse  & Distributed Systems Maestro orchestrator :  Large datasets demand parallel processing.  Python or Java can wield the power of distributed systems like Hadoop or Spark to tackle massive data volumes?

➡️Data Whisperer & Visualization Virtuoso :  Extracting insights is only half the battle.  Can you bridge the gap between complex data and clear, compelling visuals using tools like Tableau or Power BI?

✅Now, it's your turn! Which of these combos do YOU think is the most in-demand skill for a data engineering analyst?

Looking forward to an engaging conversation ahead❗️

#dataengineering #cloudcomputing #analysts #bigdata #visualization #programming #data

With the evolving landscape of Generative AI, will AI truly replace the need for programming skills altogether❓

👉According to me, it is undeniable that the demand for programming skills is the ultimate need. While AI might handle repetitive coding tasks in the future, a deep understanding of code and algorithms will remain crucial. 

🙈We'll shift from code monkeys to code whisperers.

Where, the focus from core programming can shift towards:

➡️Strong problem-solving skills: Identifying the right problems to solve with AI and designing effective pipelines.
➡️Data Literacy: A deep understanding of data management, data quality, and data pipelines.
➡️Critical thinking: Evaluating and interpreting AI outputs, ensuring data quality and addressing potential biases.
➡️Communication and collaboration: Working effectively with AI specialists, business stakeholders, and other data professionals.

♐️This post is Inspired by Alex Wang, sharing few informative resources to upskill as programmers.

🔰Introduction to Computer Science and Programming Using Python (MIT OpenCourseWare): https://lnkd.in/g-TymTiG
🔰Python for Everybody Specialization (University of Michigan): https://lnkd.in/gdDrtueZ
🔰UC San Diego's Python Programming for Everybody Specialization (https://lnkd.in/gdDrtueZ)

What are your thoughts, data professionals? Share your thoughts in the comment section!

#data #programming #python #artificialintelligence #innovation

🧑🏻‍💻Why Business Impact Matters to Data Engineers❓

I have spent a lot of time building pipelines and wrangling data. 

♐️But the secret is to connect the dots between our technical skills and the business impact of our work.

Why is this important?
As per my experience in past 6 years here's my take:

➡️Stronger Interviews: Many interviewers, myself included, love to delve into project specifics. We want to see that you understand how your project contributes to business growth. Knowing details like specific columns and their functions demonstrates a deeper understanding beyond just the code.

➡️Bridging the Gap: Business stakeholders rarely speak in technical jargon. They might ask for specific transactions from last month." Understanding the project thoroughly allows you to translate those requests into specific data queries (filter by "customer_id" and "groupBy" on "date").

➡️Greater Value: Data engineering isn't just about building pipelines; it's about empowering informed decisions. When you grasp the business impact of your work, you can take ownership and suggest improvements or optimizations.

Knowing the data value that is impacting the business solutions and delivering outcomes.

Earlier my focus was to build pipelines and focus on solutions leveraging technologies. Now, I prefer prioritizing the "why" behind every project.

The key takeaways are:

📍Go beyond the code: During knowledge transfer (KT) or self-learning, ask questions about the project's business goals and the role each data point plays.

📍Think like a translator: Prepare for business-speak in conversations. Understanding data pipelines lets you interpret their requests into actionable queries.

📍Own your impact: Become a data champion who connects technical solutions to real-world business results.

Share you experiences on how data and domain expertise helps you leverage the curiosity to deliver business values above the code and technical expertise.

#data #engineering #business #growth #innovation

Vector databases are on the rise, Are data engineers supposed to emphasize and upskill with vector databases? 

👉Not pretty sure if data engineers do need to consider this as an important skill, whereas Vector databases are specifically designed to efficiently store, index, and search these vectors. They excel at:

➖Similarity searches: Finding similar data points within massive datasets. This is crucial for tasks like recommendation systems and nearest neighbor algorithms.

➖High-dimensional data: Vector databases handle complex data structures efficiently, making them ideal for advanced ML applications.

➖Speed: Performing searches at lightning-fast speeds, essential for real-time applications.

But do data engineers need to precisely get dominated and upskill themselves with the rise of vector databases in the industry? 

👩‍💻Though Data engineers can leverage vector databases in various ways:

📌Building Scalable Search Applications: Powering search functionalities in e-commerce platforms or media recommendation systems.
📌Optimizing Machine Learning Pipelines: Streamlining data retrieval for training and deploying ML models.
📌Personalizing User Experiences: Developing recommendation engines that suggest relevant products or content to users.

✅Few popular vector databases to explore can be Milvus, Weaviate and SingleStore 

🤔 I'm actually curious and looking to know more about this topic as the trend seems to be quite competitive and is evolving pretty fast. 

Share your thoughts on the same.

#data #engineering #innovation #growth #vectordatabases

Xinran Waibel 

I really found your post intriguing and would like to know what the #dataengineering community thinks!!

#engineering #bigdata

Taplio

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