Data Extraction using Apache Nifi vs Apache Spark vs Apache Airflow

Direct throughput comparisons between these tools can be complex due to various factors like hardware, data volume, complexity of transformations, and specific use cases. This response provides a general overview based on typical scenarios.

Apache NiFi

  • Strengths:
    • Designed for high throughput and low latency data ingestion and processing.
    • Excellent for real-time data pipelines.
    • Handles large volumes of data efficiently.
  • Weaknesses:
    • Less flexible for complex data transformations compared to Spark.
    • Might be less suitable for batch processing heavy workloads.

Apache Spark

  • Strengths:
    • Powerful for complex data transformations and analytics.
    • Excellent for batch processing and large-scale data processing.
    • Can handle both structured and unstructured data.
  • Weaknesses:
    • Might have higher latency compared to NiFi for real-time processing.
    • Requires more complex setup and configuration.

Apache Airflow

  • Strengths:
    • Orchestrates complex workflows involving multiple systems.
    • Provides visibility and control over data pipelines.
    • Flexible for batch and real-time processing.
  • Weaknesses:
    • Not designed for high-throughput data processing itself.
    • Relies on underlying systems like Spark for heavy data processing.

Key Factors Affecting Throughput

  • Data Volume and Velocity: The amount and speed of data significantly impact performance.
  • Data Format and Complexity: Structured data is generally easier to process than unstructured data.
  • Hardware Resources: CPU, memory, and disk I/O capabilities influence throughput.
  • Network Bandwidth: Data transfer speed between components affects overall performance.
  • Data Transformations: Complex transformations can impact throughput.

General Considerations

  • NiFi is often the first choice for high-throughput data ingestion and simple transformations.
  • Spark is preferred for complex data processing, machine learning, and batch workloads.
  • Airflow is ideal for orchestrating complex workflows involving multiple tools, including NiFi and Spark.

In many cases, a hybrid approach combining these tools can optimize performance. For example, NiFi can ingest data, Spark can perform complex transformations, and Airflow can orchestrate the overall workflow.

To get a more accurate comparison for your specific use case, it's recommended to conduct benchmarks with representative data and workloads.

Loading CSV to SQL SERVER using Apache NIFI

Apache NiFi is a powerful tool for data ingestion, processing, and distribution. It excels at handling large datasets and complex data flows. When it comes to loading CSV files into SQL Server, NiFi offers a robust and flexible solution.

Basic Workflow

A typical NiFi flow for loading CSV data into SQL Server might involve the following processors:

  1. GetFile: This processor retrieves CSV files from a specified directory.
  2. ConvertCSVToAvro: (Optional) Converts CSV data to Avro format for improved efficiency and schema enforcement.
  3. PutDatabaseRecord: Inserts the CSV data into a SQL Server database. This processor is efficient for handling large datasets.

Key Considerations and Best Practices

  • CSV File Format: Ensure the CSV file has consistent delimiters (e.g., comma, tab), encodings (e.g., UTF-8), and column headers.
  • SQL Server Connection: Configure the PutDatabaseRecord processor with the correct database connection properties (JDBC driver, URL, username, password).
  • Schema Mapping: Define the mapping between CSV columns and SQL Server table columns. NiFi provides flexible options for schema configuration.
  • Error Handling: Implement error handling mechanisms to address potential issues like invalid data, database connection failures, or processing errors.
  • Performance Optimization: Consider using batching, compression, and indexing to improve performance for large datasets.
  • Data Validation: Validate the CSV data before loading it into the database to ensure data quality and consistency.
  • Security: Protect sensitive data by encrypting it during transmission and storage.
  • Scheduling: Schedule the data flow to run at specific intervals or based on triggers.

Advanced Features and Considerations

  • Bulk Loading: For extremely large datasets, consider using bulk loading options provided by SQL Server to improve performance.
  • Data Transformation: If required, use NiFi processors like UpdateAttribute, ReplaceText, or ExecuteScript to transform data before loading it into SQL Server.
  • Data Quality: Employ data quality processors like ValidateCSV or ValidateRecord to check data integrity and consistency.
  • Incremental Loads: Implement logic to handle incremental loads by tracking the last processed file or timestamp.
  • Error Handling and Retry: Configure retry mechanisms and dead-letter queues to handle failed records and prevent data loss.
  • Monitoring and Logging: Use NiFi's monitoring capabilities to track data flow, performance, and error metrics.

Example NiFi Flow

A typical NiFi flow would include:

  1. GetFile: Reads CSV files from a specified directory.
  2. ConvertCSVToAvro: (Optional) Converts CSV to Avro for better performance.
  3. PutDatabaseRecord: Inserts Avro records (or CSV records directly) into SQL Server.

Additional Tips

  • Use NiFi's expression language to dynamically configure processor properties based on flow file attributes.
  • Leverage NiFi's reporting capabilities to generate reports on data loading metrics.
  • Consider using NiFi's provenance feature to track data lineage.

By following these guidelines and leveraging NiFi's capabilities, you can efficiently and reliably load CSV data into SQL Server.

Professional Benefits of Partnering with a Google Summit Conference

 

Professional Benefits of Partnering with a Google Summit Conference

Partnering with a Google Summit Conference can offer substantial benefits to a company or organization. Here are some key advantages:

Brand Enhancement and Visibility

  • Association with Google: Aligning your brand with Google's reputation for innovation and technological leadership can significantly enhance your brand's image.
  • Increased Visibility: Partnering with a Google Summit provides a platform to showcase your products or services to a large and influential audience.
  • Networking Opportunities: Connect with potential customers, partners, and industry leaders who attend the conference.

Market Insights and Industry Trends

  • Access to Industry Experts: Gain valuable insights from Google experts and industry leaders through keynote speeches, workshops, and panel discussions.
  • Understanding Market Dynamics: Stay updated on the latest market trends, customer behaviors, and technological advancements.
  • Identifying New Opportunities: Discover potential business opportunities and partnerships by understanding the evolving landscape.

Lead Generation and Customer Acquisition

  • Generating Qualified Leads: Utilize the conference as a platform to generate high-quality leads and nurture potential customers.
  • Building Relationships: Interact with attendees to build strong relationships and foster customer loyalty.
  • Demonstrating Expertise: Position your company as a thought leader in your industry through presentations, workshops, or sponsorships.

Talent Acquisition and Employer Branding

  • Attracting Top Talent: Showcase your company as an innovative and forward-thinking employer to potential employees.
  • Building Employer Brand: Enhance your company's reputation as a great place to work by participating in the conference.
  • Networking with Potential Employees: Connect with skilled professionals who attend the conference.

Product and Service Promotion

  • Launching New Products or Services: Utilize the conference as a platform to launch new products or services and generate buzz.
  • Demonstrating Product Capabilities: Showcase your products or services through interactive demos, exhibits, or workshops.
  • Gathering Feedback: Gather valuable feedback from attendees to improve your offerings.

Strategic Partnerships and Collaborations

  • Finding Complementary Partners: Identify potential partners who can help you expand your market reach or product offerings.
  • Collaborating on Joint Initiatives: Explore opportunities for co-marketing, product development, or research collaborations.

By strategically aligning your company's goals with the conference's objectives, you can maximize the benefits of partnering with a Google Summit Conference.

Apache Nifi 2.0

 

Apache NiFi 2.0 is around the corner and it is going to bring a lot of new features that I, personally, am very excited about. We can mention key features such as:

  • Making Python a first class citizen in NiFi, allowing Python aficionados to easily and quickly build NiFi components in Python. This will bring closer the data engineers and the data scientists communities as well as greatly expanding the set of use cases that NiFi can cover.
  • Giving the possibility to run a Process Group using NiFi Stateless. NiFi Stateless has been around for quite some time but had never been easy to use until NiFi 2.0 where you can enable it at process group level. This gives the opportunity to use NiFi for critical use cases where a flow should be considered as a transaction (think CDC for example).
  • Rules Engine to provide feedback to the flow designers with regard to best practices, to give recommendations for the configuration of the components, etc.

However this major new release of NiFi comes with breaking changes and you can take actions starting today to be in the best possible spot for when you want to upgrade to NiFi 2.0. The below list is not exhaustive but gives a good starting point. You can find out more about the NiFi 2.0 goals by going here.

Note that the community is planning to build some tooling to automate as many of those actions as possible but it’s for the best if you go through this list and take actions on your side. While there will be tooling to help, there could always be some edge cases we can’t cover / anticipate.

Java 21

NiFi 2.0 will only support Java 21 so you want to make sure you have Java 21 installed on your NiFi nodes before upgrading. Note that you can already use the latest 1.x releases of NiFi with Java 21.

Templates

The concept of templates is going away. The XML templates are stored in memory in NiFi as well as in the persisted flow definition (flow.xml.gz & flow.json.gz files) and it caused a lot of problems with some of our biggest NiFi users when they had tens or hundreds of massive templates (with thousands of components). Removing all of this will bring a lot more stability to NiFi and improve memory usage.

If you have templates, you will want to export those templates as JSON definitions or version the templates into a NiFi Registry instance. The best practice is really to use a NiFi Registry in combination with NiFi when it comes to version control and share / reuse flow definitions.

To do that:

  • If your template is a process group, you can just drag and drop the template on the canvas and then right click on it and export it as a flow definition (JSON file) or start version control in your NiFi Registry if you have one configured.
  • If your template is not a process group but directly a flow with components, you’d want to drag and drop a process group, then go into that process group and drag and drop your template there. You can then go back to the parent process group containing your template and export it as a flow definition or start version control on it.

Variables

Variables and Variable Registry are going away. It was coming with a lot of limitations such as the need for expression language support in a property to reference a variable, and the impossibility to store sensitive values. Parameter Contexts have been around for a while now and have been improved over the last few years. For example, we recently added the concept of Parameter Context Provider to source the value of parameters from external stores (like HashiCorp, Vaults of the cloud providers, etc).

Make sure to spend some time moving from variables to parameters. This is most likely the most impacting change that will require some rework on the flows. It’s a good opportunity to think about the right approach and how to split parameters into multiple Parameter Contexts and use inheritance between the contexts when you want to share the same parameters across multiple use cases.

Event Driven Scheduling Strategy

The Event Driven Scheduling Strategy was an option available on some processors. This was an experimental feature in NiFi and didn’t prove to bring any significant performance improvements. This feature is going away with NiFi 2.0.

If you have components configured with this scheduling strategy (you can find those using the search bar in NiFi by typing: event), and update those components to use the “Timer Driven” scheduling strategy instead.

Removed Components

We’re using NiFi 2.0 as an opportunity to remove a lot of components that were deprecated and for which better alternatives are available. The exhaustive list can be found here and the community is also providing migration steps here. If your flows are using some of those processors, you probably want to start using the alternatives to make the upgrade seamless. Otherwise the components would become “ghost components” when upgrading to the new version of NiFi.

Note: if you’re running a recent version of NiFi, a dedicated log file has been added: nifi-deprecation.log. It can be a good place to review for runtime usage of features that are targeted for removal. More details can be found here.

Custom Components

If you have built custom components, you’d likely want to update the dependencies in your components to reference the NiFi 2.0 APIs and rebuild the components with a newer version to make sure they’re working properly after the upgrade. A command has been added to the CLI toolkit in NiFi to recursively change the version of all instances of a co                mponent to a newer version.

Conclusion

There is a lot of great things coming with NiFi 2.0 and you don’t want to miss any of these. Be ready and start planning for the upgrade for when NiFi 2.0 comes out. I hope this overview has been helpful and you can always reach out to the NiFi community via the mailing lists or on Slack!


Tips

- Add single user on windows 

java -cp 'lib/bootstrap/*' -Dnifi.properties.file.path=conf/nifi.properties org.apache.nifi.authentication.single.user.command.SetSingleUserCredentials admin pass

Data Governance - Oracle vs SAP

 Both Oracle ERP (including E-Business Suite) and SAP ERP offer functionalities to support data governance practices within their respective ecosystems. Here's a breakdown of their approaches:

Oracle ERP Data Governance:

Strengths:

  • Integration with Oracle Tools: Oracle offers separate products like OEDMb (data lineage, impact analysis, data quality) and EDG (data security, discovery) that integrate well with Oracle ERP.
  • Focus on Data Quality: OEDMb's data quality management functionalities provide tools to identify and address data quality issues within Oracle ERP.
  • Data Lineage Tracking: OEDMb helps track the origin and flow of data through Oracle ERP, facilitating impact analysis for data changes.

Weaknesses:

  • Fragmented Approach: Data governance functionalities are spread across different Oracle products, requiring separate licenses and potentially complex integration.
  • Limited Out-of-the-Box Functionality: Oracle ERP itself offers limited built-in data governance tools compared to SAP ERP. Organizations need to rely heavily on additional Oracle products.

SAP ERP Data Governance:

Strengths:

  • Integrated Approach: SAP offers more built-in data governance functionalities within its core SAP ERP platform, reducing reliance on separate products.
  • Strong Data Stewardship Features: SAP provides tools and workflows for data ownership definition, data quality monitoring, and data change management processes.
  • User-friendly Interfaces: SAP ERP data governance features often come with user-friendly interfaces designed for business users, not just technical specialists.

Weaknesses:

  • Limited Data Lineage Tracking: While SAP offers data lineage capabilities, they might not be as comprehensive as Oracle's OEDMb solution.
  • Potential Cost: SAP's built-in data governance features might be part of higher-tier licensing packages, potentially increasing costs for some businesses.

Here are some additional factors to consider when comparing the two:

  • Existing Infrastructure: If you're already heavily invested in Oracle technology, leveraging Oracle's data governance tools might offer a smoother integration into your environment.
  • Specific Needs: If data quality is a major concern, Oracle's focus on this aspect might be advantageous. However, if user-friendliness and data stewardship features are priorities, SAP might be a better choice.
  • Budget: Compare the costs of licensing additional Oracle products like OEDMb and EDG versus the potential cost of higher-tier SAP ERP packages that include data governance features.

Ultimately, the best choice depends on your specific data governance needs, existing infrastructure, and budget constraints. Both Oracle and SAP offer viable solutions, but careful evaluation is necessary to determine the most suitable option for your organization.

Installing Spark on Windows 10

 

Prerequisites

  • Install and configure hadoop

 

1.Download Apache Spark
  • https://spark.apache.org/downloads.html
  • Under the Download Apache Spark heading, there are two drop-down menus. Use the current non-preview version.
  • In our case, in Choose a Spark release drop-down menu select 2.4.5
  • In the second drop-down Choose a package type, leave the selection Pre-built for Apache Hadoop 2.7
  • Click the spark-2.4.5-bin-hadoop2.7.tgz link

02. Create Folder path ‘C:\Spark’ and Extrcat the downloaded Spark file from ‘Download’ folder to ‘C:\Spark’

03. Set in the ‘Environment Variable’

04. Launch Spark

  • Open a new command-prompt window using the right-click and Run as administr
  • Run below the command ‘spark-shell’ from C:\Spark\bin

4.1 Finally, the Spark logo appears, and the prompt displays the Scala shell.

4.2 Open a web browser and navigate to http://localhost:4040/

4.3 To exit Spark and close the Scala shell, press ctrl-d in the command-prompt window.

4.4 Start Spark in ‘Pyspark’ as Shell

The PySpark shell is responsible for linking the python API to the spark core and initializing the spark context.

5. Start Master and Slave

  1. Setup and Run Spark Master and Save on the Machine (Standalone)
  • Run Master
  • — — Open the ‘command Prompt’ from the path ‘C:\Spark\bin’
  • — — Run Below the command

C:\Spark\bin>spark-class2.cmd org.apache.spark.deploy.master.Master

C:\Spark\bin>spark-class org.apache.spark.deploy.master.Master

2. Run Slave

  • Open the command Prompt from the path ‘C:\Spark\bin’
  • Run Below the command

C:\Spark\bin>spark-class2.cmd org.apache.spark.deploy.worker.Worker -c 1 -m 4G spark://10.0.0.4:7077

Note : Make Sure Master and Slave Command Prompt are running

6. Web GUI

  • Apache Spark provides suite Web UI for monitor the status of your Spark/PySpark application, resource consumption of
    Spark cluster, and Spark configurations.
  • Apache Spark Web UI

— Jobs
— Stages
— Tasks
— Storage
— Environment
— Executors
— SQL

Open a web browser and navigate to http://localhost:4040/

Note : Master and Slave should be started

Create A python program as below and save it as spark_basic.py on the desktop

  • spark_basic.py
    import findspark
findspark.init('C:\Spark')

from pyspark import SparkConf
from pyspark import SparkContext

conf = SparkConf()
conf.setMaster('spark://10.0.0.4:7077') # Mention the Master Node
conf.setAppName('spark-basic')
sc = SparkContext(conf=conf)

def mod(x):
import numpy as np
return (x, np.mod(x, 2))

rdd = sc.parallelize(range(1000)).map(mod).take(10)
print(rdd)

Refresh the master WebGUI

Refresh the Slave WebGUI

Note: Make Sure While Running Spark Application (Code from python File) Master and Slave are runnning