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Lets say you have 5 TB of data stored in your Amazon S3 bucket consisting of 500 million records and 100 columns.

Now, suppose there are 100 cities and you want to get the data for a particular city, and you want to retrieve only 10 columns.

~ considering each city has equal amount of records,
we want to get 1% of data in terms of number of rows
and 10% in terms of columns

thats roughly 0.1% of the actual data which might be 5 GB roughly.

Now lets the pricing if you are using serverless technology like AWS Athena

- the worst case you end up having the data in a csv format (row based) with no compression. you end up scanning the entire 5 TB data and you pay $25 for this query. (The charges are $5 for each TB of data scanned)

Now lets try to improve it..

- use a columnar file format like parquet with snappy compression which takes lesser space so your 5 TB data might roughly become 2 TB (actually it will be even lesser)

- partition this based on city so that we have 1 folder for each city.

This way you have 2 TB data sitting across 100 folders, but you have to scan just one folder which is 20 GB,

Not just this you need 10 columns out of 100 so roughly you scan 10% of 20 GB (as we are using columnar file format)

This comes out to be 2 GB only.

so how much do we pay?
just $.01 which is 2500 times lesser than what you paid earlier.

This is how you save cost.

what we did?

- using columnar file formats for column pruning
- using partitioning for row pruning
- using efficient compression techniques

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SNOWFLAKES AND DATABRICKS

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?

🌐  𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞

❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.

❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
❄️  Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.

❄️  𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.

❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.

🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬

❄️  𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.

❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.

🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.

❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.

❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous.

🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:

Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

Data Engineering Tools:

Apache Hadoop 🗂️ – Distributed storage and processing for big data

Apache Spark ⚡ – Fast, in-memory processing for large datasets

Airflow 🦋 – Orchestrating complex data workflows

Kafka 🐦 – Real-time data streaming and messaging

ETL Tools (e.g., Talend, Fivetran) 🔄 – Extract, transform, and load data pipelines

dbt 🔧 – Data transformation and analytics engineering

Snowflake ❄️ – Cloud-based data warehousing

Google BigQuery 📊 – Managed data warehouse for big data analysis

Redshift 🔴 – Amazon’s scalable data warehouse

MongoDB Atlas 🌿 – Fully-managed NoSQL database service

React ❤️ for more

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𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍

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Data Analyst vs Data Engineer: Must-Know Differences

Data Analyst:
- Role: Focuses on analyzing, interpreting, and visualizing data to extract insights that inform business decisions.
- Best For: Those who enjoy working directly with data to find patterns, trends, and actionable insights.
- Key Responsibilities:
- Collecting, cleaning, and organizing data.
- Using tools like Excel, Power BI, Tableau, and SQL to analyze data.
- Creating reports and dashboards to communicate insights to stakeholders.
- Collaborating with business teams to provide data-driven recommendations.
- Skills Required:
- Strong analytical skills and proficiency with data visualization tools.
- Expertise in SQL, Excel, and reporting tools.
- Familiarity with statistical analysis and business intelligence.
- Outcome: Data analysts focus on making sense of data to guide decision-making processes in business, marketing, finance, etc.

Data Engineer:
- Role: Focuses on designing, building, and maintaining the infrastructure that allows data to be stored, processed, and analyzed efficiently.
- Best For: Those who enjoy working with the technical aspects of data management and creating the architecture that supports large-scale data analysis.
- Key Responsibilities:
- Building and managing databases, data warehouses, and data pipelines.
- Developing and maintaining ETL (Extract, Transform, Load) processes to move data between systems.
- Ensuring data quality, accessibility, and security.
- Working with big data technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, Google Cloud).
- Skills Required:
- Proficiency in programming languages like Python, Java, or Scala.
- Expertise in database management and big data tools.
- Strong understanding of data architecture and cloud technologies.
- Outcome: Data engineers focus on creating the infrastructure and pipelines that allow data to flow efficiently into systems where it can be analyzed by data analysts or data scientists.

Data analysts work with the data to extract insights and help make data-driven decisions, while data engineers build the systems and infrastructure that allow data to be stored, processed, and analyzed. Data analysts focus more on business outcomes, while data engineers are more involved with the technical foundation that supports data analysis.

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