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Data automation: Best practices and implementation

Data automation has emerged as a crucial approach for organizations aspiring to optimize their data management workflows in our current data-focused era. Businesses can use data automation to boost operational efficiency, enhance productivity, and extract invaluable insights from extensive datasets.

This comprehensive article is designed to dive deep into the various facets of data automation, demystifying its mechanisms and detailing its transformative effects on the modern data ecosystem.

Decoding data automation

Data automation involves using software tools to automate routine data management tasks, such as data ingestion, data transformation (ETL processes), data quality checks, data governance, and data analysis. It enables organizations to significantly increase processing speed, improve data accuracy, and reduce manual intervention. 

While coding can achieve a certain level of automation, platforms like Nexla offer comprehensive, user-friendly, and feature-rich solutions that simplify the automation of these complex processes.

In data automation, several best practices have been established to ensure the robustness and reliability of data operations. These practices revolve around various aspects of data management, including ingestion, transformation, quality checks, governance, and preprocessing for analysis. 

To truly understand the impact and application of these practices, let’s explore each of them in more detail as they are implemented.

Best practice Description
Automated data ingestion This practice involves automating the process of collecting and importing data from various sources.
Data transformation automation It’s essential to automate data transformation to ensure that data is correctly formatted for analysis.
Data quality assurance Automating data quality checks can increase the accuracy and reliability of your data.
Automated data governance Ensure data integrity, privacy, and access control through automated data governance. 

Automating data ingestion 

The first step in the data lifecycle is data ingestion: collecting and importing data from disparate data sources. Traditionally, this process can be time-consuming, error-prone, and require significant manual human intervention. However, using Python and the pandas library, we can build a data ingestion pipeline with just a few lines of code.

import pandas as pd

# Ingest a CSV file
df = pd.read_csv('path_to_your_file.csv')

# Ingest an Excel file
df = pd.read_excel('path_to_your_file.xlsx')

That said, while this example paints a neat picture of data ingestion, the process is usually muddied. In the trenches of building data automation pipelines, data engineers are faced with diverse data sources, finicky API rules, a variety of databases, and varying data velocities, changing schemas, erroneous records, to name a few of the difficulties. Each of these challenges can break a data ingestion pipeline, making the system brittle, requiring human intervention, and hence effectively unautomated. High-quality automation needs to be resilient and even self-correcting.

Methods for automating data ingestion 

What are the solutions to addressing the challenges of data ingestion and making it automated? Data engineers can arm themselves with an arsenal of automation methods to streamline this process: 

  • Programmatic automation: This method is perfect for those who like to have granular control over each step. Languages like Python, used with libraries like pandas, can make data ingestion easier. 
  • Task schedulers: Ever wish you could set your data tasks on autopilot? Task schedulers like cron in Unix or the Windows Task Scheduler do just that. 
  • Extract, transform, and load (ETL) tools: These tools are the Swiss Army knives of data automation. Think of them as your one-stop shop, handling everything from data extraction to its final resting place in a database. Platforms like Talend or Apache NiFi are players in this space.
  • Data pipeline platforms: Imagine having a conveyor belt for your data, moving it seamlessly from point A to point B while performing all sorts of manipulations in between. That’s what data pipeline platforms like Nexla offer. They’re robust, end-to-end solutions for those who’d rather not get caught in the weeds.

Automating data ingestion: code vs. no-code and low-code 

When it comes to automating data ingestion, you have choices: Dive deep into code or take the no-code or low-code route with platforms like Nexla. Let’s weigh these options by illustrating a real-world example—extracting data from Salesforce API and loading it into Snowflake.

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The code-heavy approach: Using Python and pandas

You can roll up your sleeves and write code using Python libraries like pandas, as shown below. This script automates data ingestion with pandas by extracting data from a Salesforce API and loading it into Snowflake. 

import pandas as pd
import requests
from snowflake.connector.pandas_tools import write_pandas
import snowflake.connector

# Setup for the Salesforce API request
url = "https://your_instance.salesforce.com/services/data/vXX.0/query"
query = "SELECT * FROM Sales_Data"
headers = {
    "Authorization": "Bearer your_auth_token",
    "Content-Type": "application/json"
}

# Make the request to the Salesforce API to get the sales data
response = requests.get(url, headers=headers, params={"q": query})

# Convert the response into a pandas DataFrame
data = response.json()["records"]
df = pd.DataFrame(data)

# Perform some data transformation (for example, updating the sales amount)
df['Sales_Amount'] = df['Sales_Amount'].apply(lambda x: x*1.2)

# Establish a connection to Snowflake
conn = snowflake.connector.connect(
    user='username',
    password='password',
    account='account_url',
    warehouse='warehouse_name',
    database='database_name',
    schema='schema_name'
)

# Write the DataFrame to a Snowflake table
success, nchunks, nrows, _ = write_pandas(conn, df, 'sales_table')

Using bash to execute your Python script 

Next, wrap your data extraction Python script in a bash script, so you can automate this data task with a cron job. Below is an example of a bash script to execute the Python script we just created.

#!/bin/bash
# bash_script.sh

# Navigate to the directory of your Python script (modify this according to your Python script's location)
cd /path/to/your/python/script

# Execute your Python script
/usr/bin/python3 data_ingestion.py

Cron job for scheduling

Finally, you can set your bash script to run on a schedule. Here, we set it to run every day at 2:00 am:

# This will run your script every day at 2 a.m.
0 2 * * * /path/to/your/bash/script/bash_script.sh >> /path/to/your/log/file.log 2>&1

The no-code or low-code approach using Nexla

If you’d rather skip the coding obstacle course, platforms like Nexla provide an intuitive interface for automating all of your data ingestion needs. You can connect to sources like SaaS platforms, databases, and APIs—no code required.

Here are the key features:

  • Automated data ingestion: Nexla handles data in any format and supports real-time and batch processing.
  • Active metadata: Makes connecting to new data sources a cinch, optimizing throughput and handling complexities like pagination of APIs.
  • Universal connectors: Your single source for data ingestion regardless of the source.

Nexla’s universal connectors (source)

Nexla’s universal connectors (source)

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Streamlining data transformation with automation

Data transformation doesn’t have to be daunting. Automation can speed things up, enhance accuracy, and let you focus on the fun stuff—like data analysis. Let’s dive into the ETL process and how to make it run like a well-oiled machine.

The role of ETL in data management

ETL matters because you can think of it as the backbone of data management, especially in data warehousing. Automating this three-step process—extracting, transforming, and loading—reduces human error, boosts data integrity, and gets things done quickly.

Methods for automating ETL processes

Data engineers have a plethora of options for ETL automation, ranging from Apache Beam and Airflow to Google Cloud Data Flow and Informatica PowerCenter.

Let’s kick off with Apache Beam, a tool for batch and stream data processing. Shown below is a simple code example that takes a CSV file, multiplies each value by 10, and writes back the results.

import apache_beam as beam

# Define the pipeline
p = beam.Pipeline()

# Define a simple transformation function
def multiply_by_ten(element):
    return element * 10

(p 
 | "Read from CSV" >> beam.io.ReadFromText('input.csv') 
 | "Split rows" >> beam.Map(lambda row: row.split(',')) 
 | "Convert to int and multiply" >> beam.Map(lambda row: [int(number)*10 for number in row]) 
 | "Convert back to CSV" >> beam.Map(lambda row: ','.join(str(number) for number in row))
 | "Write to new CSV" >> beam.io.WriteToText('output.csv')
)

# Run the pipeline
p.run()

No-code and low-code, metadata-driven ETL automation

Why code your way through ETL when automation via metadata intelligence can do the heavy lifting? If you’re really invested in schema management, choose a no-code or low-code platform that provides a comprehensive suite of tools to keep your schema neat and functional. Here’s how modern no-code or low-code data operations platforms, such as Nexla, simplify and automate the ETL process:

  • Schema detection: Automatically detects your schema, saving you the headache of manual mapping. The system intelligently identifies column types, relational constraints, and field patterns. This is particularly useful in complex, nested JSON or XML structures where manual schema detection is time-consuming and error-prone.
  • Schema evolution handling: Adaptive detection capabilities that evolve with your data schema so you’re not stuck reconfiguring settings. Thanks to its underlying metadata management layer, the platform makes use of schema versioning and automated data lineage tracking to adapt to schema changes without manual reconfiguration.
  • Data transformation: Robust data transformation features ensure that your data is in the shape and format you need. The platform can handle transformations at scale by making use of parallel processing and optimized execution plans. It also supports advanced transformations using custom Python, SQL, or Java expressions.

Nexla schema management (Source)

Nexla schema management (Source)

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Ensuring data quality through automation

Data quality isn’t just another item in your backlog; it’s a competitive edge. Quality matters, and automation can help you ensure it is consistent, efficient, and without the tedious manual work. By automating data quality checks and fixes, you free up resources and ensure a high level of consistency and efficiency.

Leveraging Python and pandas for implementing data quality 

Python and pandas are the dynamic duo for data quality: Python, fortified by its powerful pandas library, is an excellent tool for automating data quality checks. A few lines of code can catch an array of issues, from missing values and duplicates to more complex inconsistencies. Let’s explore how this works.

The following is a Python script that ingests a data set, detects missing values, identifies duplicates, and verifies data types for consistency. 

import pandas as pd

# Sample data ingestion
df = pd.read_csv('data.csv')

# Detecting missing values
missing_values = df.isnull().sum()

# Identifying duplicates
duplicates = df.duplicated().sum()

# Verifying data types for consistency
inconsistent_data_types = df.dtypes

When things get complex—like dealing with date formats that “play hard to get”—you can easily adjust your Python code to restore uniformity. For instance, suppose we have a dataset with date entries in inconsistent formats (“MM-DD-YYYY” and “YYYY-MM-DD”):

data = {
    'date': ['01-30-2020', '2020-01-31', '02-01-2020', '2020-02-02'],
    'value': [100, 150, 200, 250]
}
df = pd.DataFrame(data)

With a bit of Python wizardry, your DataFrame can display dates in the standardized format of your choice. Here is an example of a Python script to rectify the data inconsistencies coming from a dataset like the one above:

import dateutil

# Correcting inconsistent date formats
def correct_date_format(date_string):
    return dateutil.parser.parse(date_string).strftime('%Y-%m-%d')

df['date'] = df['date'].apply(correct_date_format)

Using the above example, the corrected DataFrame would show dates uniformly:

       date       value
0   2020-01-30    100
1   2020-01-31    150
2   2020-02-01    200
3   2020-02-02    250

Here’s the kicker, though: Maintaining these scripts can become a handful when you’re sifting through mountains of data. Enter modern tools like Nexla, a platform tailored for automating data operations, including quality checks.

How to take data quality up a notch with automation

Why wrestle with code when modern data operation platforms can do it for you? Nexla shines in automating data quality checks, offering several key features in this area:

  • Automated data mapping: Utilizes algorithms to analyze the data schema automatically. It intelligently recognizes data types, relationships, and constraints, which is essential for accurate data mapping and transformation.
  • Prebuilt quality rules: Offers a library of preconfigured data quality rules that can be directly applied to various data sources. These rules cover a range of checks, like null value handling, data type validation, range checks, and complex logical validations.
  • Data monitoring and alerts: Data engineers can set up real-time monitoring with custom KPI-based alerts. The system can flag anomalies, outliers, or any violations of preset data quality rules, providing timely alerts through email or integrated third-party services like Slack.
  • Integration capabilities: Designed to offer robust API and SDK support for seamless integration into various data ecosystems. Whether it’s RESTful services, SOAP endpoints, or direct database connections, Nexla’s integration framework supports it.

Remember our pesky date inconsistency problem? With no-code or low-code platforms, you navigate to the interface, set a rule to standardize date formats, and you’re good to go—no code needed:

  1. Input: Same dataset with inconsistent date formats.
  2. Process: Through the platform’s interface, apply a rule to standardize date formats. No custom script required.
  3. Output: Spits out a DataFrame just like Python would. It doesn’t stop there, however: It keeps an eye out for any future inconsistencies, keeping your data in tip-top shape.

 Output validation rules (Source)

Output validation rules (Source)

Implementing data governance automation

Implementing data governance automation is essential in key areas such as trustworthiness, compliance, operational efficiency, and decision-making to ensure robust data management and bolster organizational capabilities. Here’s a closer look at these crucial dimensions:

  • Trustworthiness: Governance strategies incorporate data verification techniques, checksum validations, and cryptographic algorithms to ensure data reliability.
  • Compliance: Automated compliance checks evaluate each data transaction against a repository of regulatory rules, such as GDPR, CCPA, HIPAA, etc.
  • Operational efficiency: Data governance automates deduplication processes and integrates with enterprise data catalogs, optimizing storage and computational resources.
  • Decision-making: Governance involves automated anomaly detection, which validates the data before it feeds into decision models, thus enhancing decision-making accuracy.
  • Security: Role-based access control (RBAC), attribute-based access control (ABAC), and end-to-end encryption are used to secure data.

Having explored the pivotal aspects of automating data governance, let’s shift our focus to the tools that can help implement these governance strategies. Here are some commonly used open-source tools that specialize in automating various dimensions of data governance:

  • Apache Atlas: Metadata management and data governance
  • Amundsen: Data discovery and metadata engine
  • DataHub by LinkedIn: Extensible metadata platform
  • Metacat by Netflix: Federated data governance

Integrate with open-source tools

Modern data operation tools offer seamless integration with open-source tools like Apache Atlas and Amundsen to complement their capabilities:

  • Amundsen ensures that data is discoverable and understood, with its robust data tagging and metadata management.
  • Apache Atlas monitors lineage and compliance by leveraging built-in auditing and lineage-tracking features.

No-code or low-code platform role in automating data governance

No-code or low code platforms offer a comprehensive suite of features aimed at enhancing various aspects of data governance, positioning the platform as a powerful tool for data governance. Here are the key features that platforms like Nexla brings to the table for automating data governance:

  • Unified data access control: Uses OAuth 2.0 and SAML-based single sign-on (SSO) to ensure a unified access protocol across various data platforms.
  • Data lineage and visibility: Integrates with existing data lineage tools to provide a comprehensive view of how data flows and transforms within your ecosystem.
  • Automated data quality checks: Provides automatic outlier detection, normalization, and validation against predefined quality metrics.
  • Metadata management: Automatic extraction and indexing of metadata for all ingested data enables easier data cataloging and governance.
  • Policy enforcement and monitoring: Features like rule-based access control and real-time monitoring against predefined KPIs ensure compliance.
  • Audit trails: Comprehensive logs detail every operation on the data, which is useful for analyses and compliance audits.
  • Integration with other data governance tools: Offers robust API support and prebuilt connectors to facilitate integration with various data governance platforms. Nexla automates data masking by applying hash-tag functions to create non-sensitive Nexsets.
  • Access control: Allows fine-grained permissions at the dataset level, ensuring that only authorized users can access specific data.
  • Automatic error reporting: Supports automated error reporting by leveraging global monitoring, flow-level error notifications, and validation rules and filters. Automating error handling improves your data pipeline’s quality and reliability, reducing the need for reactive fixes.

Through implementing these features and integrations, your data operations platform plays a pivotal role in automating data governance, thereby enhancing data quality, security, and operational efficiency.

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Recap: the essential role of data automation 

Data automation is crucial for efficient and reliable data management, from ingestion to governance:

  • Data ingestion: Code-based solutions are robust but require upkeep. Metadata-driven automated integration platforms can provide a more straightforward, no-code/low-code alternative.
  • Data transformation: While open-source tools offer robust features, an automation-driven tool streamlines the process with intuitive, built-in capabilities.
  • Data quality: Python and pandas offer granular control but can be cumbersome and hard to maintain. Automation gives effective quality checks that can also be adaptive while connecting quality to error management and data quarantine in a single workflow.
  • Data governance: Open-source tools provide foundational governance, but automation-driven tools add a layer of simplicity while being comprehensive.

Nexla stands out as a game-changing tool, simplifying complex data operations and offering features seamlessly integrating with other platforms. As data’s role in business grows, adopting next-gen automation tools becomes beneficial and essential.

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