However, how do you ensure your data is squeaky clean and free from errors, inconsistencies, and inaccuracies? That’s the question we’ll explore in this blog as we prepare for our upcoming webinar,” Data Assurance: The Essential Ingredient for Data-Driven Decision Making.”

The Data Dilemma

Data comes from various sources and often arrives in different formats and structures. Whether you’re a small startup or a large enterprise, managing this influx of data can be overwhelming. Many organizations face common challenges:

1. Data Inconsistencies: Data from different sources may use varying formats, units, or terminologies, making it challenging to consolidate and analyze.

2. Data Errors: Even the most careful data entry can result in occasional errors. These errors can propagate throughout your systems and lead to costly mistakes.

3. Data Security: With data breaches and cyber threats on the rise, ensuring the security of your data is paramount. Safeguarding sensitive information is a top concern.

4. Compliance: Depending on your industry, you may need to comply with specific data regulations. Non-compliance can result in hefty fines and a damaged reputation.

The Scrubbing Approach

One way to tackle data quality issues is through data scrubbing. Data scrubbing involves identifying and correcting errors and inconsistencies in your data. This process includes tasks such as:

1. Data Cleansing: Identifying and rectifying inaccuracies or inconsistencies in your data, such as misspellings, duplicate records, or missing values.

2. Data Standardization: Converting data into a consistent format or unit, making it easier to compare and analyze.

3. Data Validation: Checking data against predefined rules to ensure it meets specific criteria or business requirements.

4. Data Enrichment: Enhancing your data with additional information or context to improve its value.

While data scrubbing is a crucial step in data quality management, it often requires manual effort and can be time-consuming, especially for large datasets. Additionally, it may not address all data quality challenges, such as security or compliance concerns.

The Testing Approach

On the other hand, data testing focuses on verifying the quality of your data through systematic testing processes. This approach includes:

1. Data Profiling: Analyzing your data to understand its structure, content, and quality, helping you identify potential issues.

2. Data Validation: Executing validation checks to ensure data conforms to defined rules and criteria.

3. Data Security Testing: Assessing data security measures to identify vulnerabilities and ensure data protection.

4. Data Compliance Testing: Ensuring that data adheres to relevant regulations and compliance standards.

Data testing leverages automation and predefined test cases to efficiently evaluate data quality. It provides a proactive way to catch data issues before they impact your business operations or decision-making processes.

Dive into the world of data assurance and understand why it’s a standalone practice in data-driven success.

Data is the most valuable asset for any business in a highly competitive and fast-moving world. Maintaining the integrity and quality of your business data is therefore crucial. However, ensuring data quality assurance often comes with its own set of challenges.

Lack of data standardization: One of the biggest challenges in data quality management is that data sets are often non-standardized, coming in from disparate sources and stored in different, inconsistent formats across departments.

Data is vulnerable: Data breaches and malware are everywhere, making your important business data vulnerable. To ensure data quality is maintained well, the right tools must be used to mask, protect, and validate data assets.

Data is often too complex: With hybrid enterprise architectures on the rise, the magnitude and complexity of inter-related data is increasing, leading to further intricacies in data quality management.

Data is outdated and inaccurate: Incorrect, inconsistent, and old business data can lead to inaccurate forecasts, poor decision making, and business outcomes.

Heterogenous Data Sources We Work With Seamlessly

With iDAF, you can streamline data assurance across multiple heterogeneous data sets, avoid data quality issues arising during the production stage, completely remove the inaccuracy and inconsistency of sample-based testing, and increase 100% data coverage.

iDAF leverages the best open-source big data tools to perform base checks, data completeness, business validation, reports testing, and 100% data accuracy.

We leverage iDAF to carry out automated validation between target and source datasets for

1. Data Quality

2. Data Completeness

3. Data Integrity

4. Data Consistency

The Perfect Blend

So, should you choose data scrubbing or data testing? Well, the answer may lie in a combination of both.

1. Scrubbing for Cleanup: Use data scrubbing to clean and prepare your data initially. This step is essential for eliminating known issues and improving data consistency.

2. Testing for Ongoing Assurance: Implement data testing as an ongoing process to continuously monitor and validate your data. This ensures that data quality remains high over time.

Join us in our upcoming webinar, “Data Assurance: The Secret Sauce Behind Data-Driven Decisions,” where we’ll delve deeper into these approaches. We’ll explore real-world examples, best practices, and the role of automation in maintaining clean, reliable data. Discover how the right combination of data scrubbing and testing can empower your organization to harness the full potential of your data.

The post Scrub or Test: What Helps in Ensuring You Have the Cleanest Data appeared first on Indium.

Given the volume of data being generated, it also comes as no surprise that the global big data and data engineering services market size is expected to grow from $39.50 billion in 2020 to $87.37 billion by 2025 at a CAGR of 17.6%.

While the availability of large volumes of unstructured data is driving this market, it is also being limited by a lack of access to data in real time. What businesses need is speed to make the best use of data at scale.

Mozart’s Modern Data Platform for Speed and Scale

One of the biggest challenges businesses face today is that each team or function has different software that is built specifically for the purpose. As a result, data is scattered and siloed, making it difficult to get a holistic view. Businesses need a data warehouse solutions to unify all the data from different sources to derive value. This requires transformation of data into a format that can be used for analytics. Often, businesses use homegrown solutions that can add to time and delays, not to mention costs.

Mozart Data is a modern data platform that enables businesses to unify data from different sources within an hour, to provide a single source of truth. Mozart Data’s managed data pipelines, data warehousing, and transformation automation solutions enable the centralization, organization, and analysis of data, proving to be 70% more efficient than traditional approaches. The modern scalable data stack comes with all the required components, including a Snowflake data warehouse.

Some of its key functions include;

• Deduplication of reports
• Unification of conventions
• Making suitable changes to data, enabling BI downstream

This empowers business users with access to accurate, clean, unified, and uniform data needed for generating reports and analytics. Users can schedule  data transformation automation in advance too. Being scalable, Mozart enables incremental transformation for processing large volumes of data quickly, at lower costs. This also helps business users and data scientists focus on data analysis, than on data wrangling.

Benefits of Mozart Data Platform

Some of the features of Mozart Modern Data Platform, that enable data transformation at scale, include:

Fast Synchronization

Mozart Data Platform allows no-code integration of data sources for faster and reliable access.

Integrate Data to Answer Complex Questions

By integrating data from different databases and third-party tools, Mozart helps business users make decisions quickly and respond in a timely manner, even as the business and data grow.

Synchronize with Google Sheets

It enables users to collaborate with others and operationalize data in a tool they’re most comfortable using: Google Sheets. It allows data to be synchronized with Google Sheets or enables a one-off manual export.

Use Cases of the Mozart Data Platform

Mozart Data Platform is suitable for all kinds of industries, businesses of any size, and for a variety of applications. Some of these include:

Marketing

Mozart enables data-driven marketing by providing insights and answers to queries faster. It creates personalized promotions and increases ROI by segmenting users, tracking campaign KPIs, and identifying appropriate channels for the campaigns.

Operations

It improves strategic decision-making, backed by data with self-service. It also automates tracking and monitoring of key business metrics. It slices and dices data from all sources and presents a holistic view of the same by predicting trends, expenses, revenues and costs.

Finance

It helpsplan expenses and incomes, track expenditure, and automate financial reporting. Finance professionals can access data without depending on the IT team and automate processes to reduce human error.

Revenue Operations

It improves revenue-generation through innovation and identifies opportunities for growth with greater visibility into all functions. It also empowers different departments with data to track performance, and allocate budgets accordingly.

Data Engineers

It encourages data engineers to build data stacks quickly and not worry about maintenance.It provides end-users with clean data for generating reports and analytics.

Indium to Build Mozart Data Platform at Scale for Your Organization

Indium Software is a cutting edge data solution provider that empowers businesses with access to data that help them break barriers to innovation and accelerate growth. Our team of data engineers, data scientists, and analysts combine technical expertise with experience to understand the unique needs of our customers and provide solutions best suited to achieve their business goals.

We are recognized by ISG as a Strong Contender for Data Science, Data Engineering, and Data Lifecycle Management Services. Our range of services include Application Engineering, Data and Analytics, Cloud Engineering, Data Assurance, and Low Code Development. Our cross-domain experiences provide us with insights into how different industries function and the data needs of the businesses operating in that environment.

FAQs

The post <strong>Mozart Data’s Modern Data Platform to Extract-Centralize-Organize-Analyze Data at Scale</strong> appeared first on Indium.

Automating Intelligent ETL with Data Live Tables

Given the fast-paced changes in the market environment and the need to retain competitive advantage, businesses must address the challenges, improve efficiencies, and deliver high-quality data reliably and on time. This is possible only by automating ETL processes.

The Databricks Lakehouse Platform offers Delta Live Tables (DLT), a new cloud-native managed service that facilitates the development, testing, and operationalization of data pipelines at scale, using a reliable ETL framework. DLT simplifies the development and management of ETL with:

• Declarative pipeline development
• Automatic data testing
• Monitoring and recovery with deep visibility

With Delta Live Tables, end-to-end data pipelines can be defined easily by specifying the source of the data, the logic used for transformation, and the target state of the data. It can eliminate the manual integration of siloed data processing tasks. Data engineers can also ensure data dependencies are maintained across the pipeline automatically and apply data management for reusing ETL pipelines. Incremental or complete computation for each table during batch or streaming run can be specified based on need.

Benefits of DLT

The DLT framework can help build data processing pipelines that are reliable, testable, and maintainable. Once the data engineers provide the transformation logic, DLT can orchestrate the task, manage clusters, monitor the process and data quality, and handle errors. The benefits of DLT include;

Assured Data Quality

Delta Live Tables can prevent bad data from reaching the tables by validating and checking the integrity of the data. Using predefined policies on errors such as fail, alert, drop, or quarantining data, Delta Live Tables can ensure the quality of the data to improve the outcomes of BI, machine learning, and data science. It can also provide visibility into data quality trends to understand how the data is evolving and what changes are necessary.

Improved Pipeline Visibility

DLT can monitor pipeline operations by providing tools that enable visual tracking of operational stats and data lineage. Automatic error handling and easy replay can reduce downtime and accelerate maintenance with deployment and upgrades at the click of a button.

Improve Regulatory Compliance

The event log can automatically capture information related to the table for analysis and auditing. DLT can provide visibility into the flow of data in the organization and improve regulatory compliance.

Simplify Deployment and Testing of Data Pipeline

DLT can enable data to be updated and lineage information to be captured for different copies of data using a single code base. It can also enable the same set of query definitions to be run through the development, staging, and production stages.

Simplify Operations with Unified Batch and Streaming

Build and run of batch and streaming pipelines can be centralized, and the operational complexity can be effectively minimized with controllable and automated refresh settings.

Concepts Associated with Delta Live Tables

The concepts used in DLT include:

Pipeline: A Directed Acyclic Graph that can link data sources with destination datasets

Pipeline Setting: Pipeline settings can define configurations such as;

• Notebook
• Target DB
• Running mode
• Cluster config
• Configurations (Key-Value Pairs).

Dataset: The two types of datasets DLT supports include Views and Table, which, in turn, are of two types: Live and Streaming.

Pipeline Modes: Delta Live provides two modes for development:

Development Mode: The cluster is reused to prevent restarts and disable pipeline retries for detecting and fixing errors.

Production Mode: Cluster restart for recoverable errors such as stale credentials or memory leak and execution is retried for specific errors.

Editions: DLT comes in various editions to suit the different needs of the customers such as:

• Core for streaming ingest workload
• Pro for core features + CDC, streaming ingest, and table updation based on changes to the source data
• Advanced where in addition to core and pro features, data quality constraints are also available

Delta Live Event Monitoring: Delta Live Table Pipeline event log is stored under the storage location in /system/events.

Indium for Building Reliable Data Pipelines Using DLT

Indium is a recognized data engineering company with an established practice in Databricks. We offer ibriX, an Indium Databricks AI Platform, that helps businesses become agile, improve performance, and obtain business insights efficiently and effectively.

Our team of Databricks experts works closely with customers across domains to understand their business objectives and deploy the best practices to accelerate growth and achieve the goals. With DLT, Indium can help businesses leverage data at scale to gain deeper and meaningful insights to improve decision-making.

FAQs

The post Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables appeared first on Indium.