Have you ever noticed the speed at which your favorite online service adapts to your preferences, offering tailored recommendations and real-time updates? Such adaptability is not just a user-friendly feature; it’s a direct result of the capabilities of real-time data processing. In today’s age, marked by rapid data exchange, swiftly analyzing and responding to information has become a fundamental aspect of modern operations. Snowflake, a cloud-based platform, has revolutionized the data landscape with its distinctive architecture and seamless scalability. In this era, where data is the new currency, the ability to ingest data in real time becomes crucial. Snowpipe streaming, a feature of Snowflake, addresses this need, ensuring that data, as soon as it arrives, is immediately available for querying and analysis. This capability not only bolsters the efficiency of data-driven decisions but also ensures that businesses can act on fresh insights without delay.

This blog offers an overview of Snowpipe streaming and dives into essential aspects of Snowflake, such as real-time data ingestion, replication strategies, and optimizing Snowpipe for peak performance. Furthermore, it addresses how Snowflake empowers businesses to efficiently analyze and act upon fresh insights, offering a comprehensive understanding of its transformative capabilities for businesses.

Snowpipe’s streaming framework

To fully understand Snowpipe’s near real-time data ingestion capabilities, let’s explore its innovative architectural framework and seamless integration with Snowflake’s cloud platform.

Snowpipe’s serverless architecture is the key feature of its highly efficient data ingestion process. This architecture eliminates the need for manual server management, simplifying the data pipeline. Users no longer have to worry about provisioning, maintaining, and scaling server instances. As a result, this approach is not only streamlined but also cost-effective, as it operates on a pay-as-you-go model. This ensures optimal resource allocation and consistent performance. Snowpipe’s serverless design takes advantage of event-driven processing, promptly responding to data source events. It automatically allocates and scales resources to handle various data workloads. This architectural choice empowers businesses to effortlessly process streaming data, enabling them to make informed, data-driven decisions and gain innovative insights through near real-time analytics.

Moreover, Snowpipe seamlessly integrates with Snowflake’s cloud-native platform, leveraging the latter’s data warehouse capabilities. This integration ensures that data ingested through Snowpipe is seamlessly integrated with Snowflake’s power and efficiency.

Real-time data ingestion

In today’s data-centric landscape, achieving a seamless data flow is no longer a luxury but a strategic imperative for businesses to thrive and evolve. To process information effectively from various sources, it’s important to comprehend the mechanism behind this process.

To ace data streaming—Snowpipe utilizes continuous data polling. Cloud storage repositories are keenly observed, with systems constantly monitoring for new data arrivals. As soon as data files turn up, they’re immediately fetched and funneled to the processing pipeline. This approach ensures all data is deeply checked and processed.

Immediate data ingestion prevails because of its compatibility with a multitude of data file formats. Whether the data is structured in forms like CSV or semi-structured data like JSON and XML, or binary formats like Avro, near real-time data processing supports them all. But how does Snowpipe make sense of diverse data? It’s through its Parsing mechanisms. The mechanisms prevail in dissecting the data of all the incoming data files, extracting relevant information, and organizing it for additional processing. Its process encompasses decoding binary formats, validating data against defined schemas, and enhancing data into a standardized format compatible with analysis.

Let’s consider, near real-time data ingestion operates as a high-speed highway, in which continuous data polling functions as the fast-moving traffic and parsing mechanisms play the role of smart toll booths along the route. This effective mechanism ensures that businesses can rapidly process and analyze the data as it flows, similar to vehicles passing through toll booths without slowing down. This optimized process empowers organizations to maintain a smooth, undisturbed journey toward their data-driven goals.

Understanding replication strategies

As we further explore Snowpipe’s capabilities, the other feature that shines in Snowflake—is Database Replication. These features enable near real-time data synchronization between databases, ensuring that updates and changes are automatically reflected on another database, thereby maintaining consistency and accuracy across their entire database structures. These mechanisms are instrumental in maintaining data reliability and accessibility.

The role of Continuous Data Protection (CDP)

Data replication strategies play a crucial role in maintaining healthy data integrity and resilience within the architecture. Continuous Data Protection (CDP) is at the heart of these replication strategies. CDP protects data against unexpected disruptions and breaches by regularly recording changes made to data, either from user interactions or from external data ingestion processes like streaming. These changes are precisely logged, creating an immediate data trail that can be conducive in scenarios like auditing and data recovery.

Time-travel ability

The other remarkable aspect of Snowflake’s data replication strategy is its Time-Travel ability. This replication feature enables users to access previously stored versions of the data, effectively retrieving data at any point in the data’s history. This not only aids in forensic analysis but also helps compare data states for making corrections when needed.

Failover mechanism

Finally, the failover mechanisms serve as a backup, ensuring that data processing remains uninterrupted. During the event of disruption or outrage, it automatically redirects data traffic to a backup cluster, maintaining downtime and assuring high availability. Replicating strategies like CDP, time travel, and failover helps businesses make informed decisions about data management, resource allocation, and disaster recovery.

Integration point: IoT devices and event sources

Integrating IoT devices and event sources is pivotal in data-driven environments. These integration points offer the means to connect and collect data from IoT devices, including machines, sensors, and other smart devices. Additionally, they integrate with event sources like Apache Kafka, enabling organizations to automate data collection, access near real-time insights, and enhance operational efficiency and the user experience.

Connectors and SDKs: Snowpipe provides an array of connectors and Software Development Kits (SDKs) designed to ease the process of integration. These connectors and SDKs function as a bridge between IoT devices, event sources, and the user’s Snowflake data platform. They streamline the process of transferring data from these sources into Snowflake, irrespective of the device or system the user employs.

Handling data streams: Snowpipe is meticulously crafted to handle data streams. It seamlessly handles data streams from event sources like Apache Kafka through an optimized process. Snowpipe constantly monitors the Kafka stream, staying alert for new data events. As soon as the data is detected, it automatically triggers the ingestion process, immediately fetching the new data events and directing them to Snowflake’s data processing pipeline without manual intervention. Due to its adaptable architecture, Snowpipe can concurrently handle data from various Kafka topics, ensuring prompt data ingestion during peak times. In the aftermath of ingestion, Snowpipe prepares the data for immediate analysis, empowering businesses to make decisions based on the most recent data streams.

Use case

Consider the application of Snowflake’s Snowpipe streaming in the healthcare domain. Wearable IoT devices, such as heart monitors and glucose meters, consistently produce crucial patient data. By leveraging Snowflake’s Snowpipe streaming, hospitals can access near real-time data, facilitating immediate alerts and prompt medical interventions. Snowflake’s capability to transform data into insights allows hospitals to discern health patterns, paving the way for more effective care. Additionally, Snowpipe’s encrypted data transmission safeguards the security of the medical data. This monitoring system, which uses Snowflake as its power source, improves patient care by encouraging a more connected patient experience.

Optimizing Snowpipe for peak performance  

Now that we have addressed the capabilities and functionality of Snowpipe, it’s also vital to understand how to harness and optimize it for peak performance. The following are a few strategies to ensure Snowpipe operates efficiently, minimizing latency and maximizing data throughput.

• Batch data: Snowpipe has the ability to process large volumes of data. Therefore, instead of ingesting data in small chunks, opt to batch them. This is conducive to Snowpipe as it reduces the number of calls, resulting in efficient data processing and reduced costs.
• Data compression: To speed up the processing, compress the data before ingesting. As Snowpipe supports various compression algorithms, choose the one that best suits your data size and type.
• Frequent maintenance: It’s a healthy practice to regularly review and update your Snowpipe configuration. As your data grows and changes, your configurations might need tweaks and adjustments to maintain peak performance.
• Network optimization: Always maintain a robust network connection between the data source and Snowflake. Network issues can substantially slow down the ingestion of data.

Unlock the power of Snowflake with Indium Software

Indium Software offers a holistic, one-stop solution that addresses all your data needs, delivering uninterrupted support and guidance throughout the entire data lifecycle. Their services include data warehousing, Snowflake implementation, migration, integration, and analytics. Going beyond mere Snowflake support, Indium Software ensures a seamless and effective experience with the platform, excelling in providing robust and governed access to your data.

The company facilitates seamless access across cloud environments and offers expert assistance for secure data sharing. With Indium Software’s profound Snowflake integration and implementation expertise, businesses can fully unlock their data’s potential, ushering in a transformative, data-driven future.

Conclusion

Snowpipe streaming is a remarkable feature within Snowflake’s ecosystem, redefining the way businesses handle and process data ingestion in near real time. By leveraging Snowpipe, organizations can swiftly access data-driven insights, enabling faster and more informed decision-making. Snowpipe empowers businesses to stay agile and competitive by responding timely to user preferences, ensuring data integrity, and bolstering availability. With Snowpipe Streaming, the future of data is within reach. Connect with experts at Indium Software today to harness the power of near real-time data.

The post Snowpipe Streaming: Real-time Data Ingestion and Replication Strategies appeared first on Indium.

Data lakes are storage systems that are designed to hold large quantities of raw, unstructured, or semi-structured data in its native format until it is demanded. The term “lake” is used to reflect the idea of a vast body of data, just like a lake with a vast body of water in its natural state. This allows organizations to store data in various formats such as files, objects, logs, sensor data, social media feeds, etc and can include data from various sources, including IoT devices, social media platforms, enterprise applications, and more.

Data lakes are designed to support various types of data analytics, including exploratory, descriptive, and predictive analytics, and can provide organizations with a more comprehensive view of their data, enabling better decision-making, and improved data insights. The raw form of data allows more flexible and agile data processing. Data lakes are usually implemented on Hadoop-based technologies like HDFS, cloud storage like Amazon S3, and use NoSQL databases like Apache Cassandra, or Apache HBase.

Pic (1) ref from: What is the Dark Web? (soscanhelp.com)

Data Warehouses are storage systems that are designed to hold structured data, such as data from transactional systems, CRM systems, or ERP systems. Data warehouses usually use a relational database management system (RDBMS), which means that data is organized into tables and can be queried using SQL. Data warehouses are typically implemented on SQL-based technologies like Oracle, Microsoft SQL Server, or Amazon Redshift. It is designed to support business intelligence (BI) activities, such as reporting, analysis, and data mining.

Thus, Data lakes are optimized for storage and batch processing whereas data warehouses are optimized for fast querying and analysis. In terms of cost, Data lakes are often less expensive to implement and maintain than data warehouses, because they use open-source technologies like Hadoop and NoSQL databases, and they do not require as much data processing and transformation. Data warehouses, on the other hand, can be more expensive to implement and maintain because they require specialized hardware and software, and they often require more data transformation and processing. Data Warehouses are well-suited for structured data and traditional data processing, Data Lakes are better suited for handling large volumes of unstructured data and more flexible data processing.

Check out this informative blog post on the ETL Testing – A Key to Connecting the dots

Let’s now deep dive into its testing practice in Digital Assurance. Data lake testing and data warehouse testing differ in several key aspects. These differences can affect the testing approach, methodologies, and tools used for each of these systems.

1. Data volume and variety: To check the accuracy and completeness of the data stored; Data lakes are designed to store a wide range of data types, including structured, semi-structured, and unstructured data from multiple sources. In comparison, data warehouses typically store only structured data that has been pre-processed and transformed to meet specific business requirements.
2. Data ingestion and processing: To verify that the data is properly loaded into both storage systems from various sources. Data lakes allow flexible data ingestion and can handle data from a variety of sources in real-time. It follows the ELT method as such: Extract, Load, and then Transform, which means that data transformation, cleaning, and processing are typically done after the data is ingested into the lake. In contrast, Data Warehouses are typically designed to store cleaned and processed data, which means that data transformation happens before the data is loaded into the warehouse. It follows our traditional ETL ( Extract, Transform, Load) processes, and may not be able to handle real-time data.
3. Testing scope: The scope of data lake testing is broader than data warehouse testing as it involves testing data ingestion, processing, storage, and retrieval from multiple sources. Data warehouse testing is typically focused on ensuring data accuracy, completeness, and consistency.
4. Testing tools: Data Lake testing may require a different set of tools than data warehouse testing. For example, data lake testing may require big data testing tools that can handle large volumes of data, while data warehouse testing may require more traditional testing tools such as SQL scripts or data quality tools.

5. Data Security: Data lakes often require a different set of security measures than data warehouses, as they store a wide range of data that may include sensitive information. Data lake testing should ensure that data is protected against unauthorized access, tampering, and theft.
6. Data Access: Data in data lakes can be accessed by a wider range of users and applications, which can make it more challenging to manage and monitor data access. Data lake testing should ensure that data access is secure, efficient, and auditable. Data warehouses are often designed to support a specific set of business intelligence and analytics applications.
7. Performance: Data retrieval in a data lake can be slower than in a data warehouse, as the data must be processed and organized before it can be analysed. Along with that, Data lakes can store petabytes of data, making performance a critical concern. Data lake testing should include performance testing to ensure that data retrieval, processing, and analysis can be performed in a timely manner.
8. Scalability: As data lakes grow, the ability to scale the system to handle larger amounts of data becomes increasingly important. Data lake testing should include scalability testing to ensure that the system can handle growing data volumes and processing needs.
9. Integration with other systems: Data lakes are often integrated with other systems, such as data warehouses, cloud services, and big data analytics tools. Data lake testing should include integration testing to ensure that data can be effectively shared and utilized by these systems.

In conclusion, data lake testing and data warehouse testing are both important for ensuring data quality and accuracy, but they have different requirements and testing needs due to the differences in the nature of the data and systems involved. We are aware that both Data Warehouse testing and Data Lake testing are emerging now in this new digital era. Data Lake testing is important because it helps to ensure that the data lake is functioning as expected, that the data is of high quality, and that the data lake is secure and compliant. By performing data lake testing, organizations can build trust in the data and use it with confidence in decision-making and analysis. With the help of the iDAF (Indium Data Assurance Framework) framework and other widely used tools on the market, Indium Software is successfully conducting Data Lake testing.

The post How does Data Lakes Testing differ from Data Warehouses Testing? appeared first on Indium.

Often, the data team needs different solutions to process unstructured data, enable business intelligence, and build machine learning models. But with the unified Databricks Lakehouse Platform, all these are unified. It also simplifies data processing, analysis, storage, governance, and serving, enabling data engineers, analysts, and data scientists to collaborate effectively.

For the cloud engineer, this is good news. Managing permissions, networking, and security becomes easier as they only have one platform to manage and monitor the security groups and identity and access management (IAM) permissions.

Challenges Faced by Cloud Engineers

Access to data, reliability, and quality, are key for businesses to be able to leverage the data and make instant and informed decisions. Often, though, businesses face the challenge of:

• No ACID transactions: As a result, updates, appends, and reads cannot be mixed
• No Schema Enforcement: Leads to data inconsistency and low quality.
• Integration with Data Catalog Not Possible: Absence of single source of truth and dark data.

Since object storage is used by data lakes, data is stored in immutable files that can lead to:

• Poor Partitioning: Ineffective partitioning leads to long development hours for improving read/write performance and the possibility of human errors.

• Challenges to Appending Data: As transactions are not supported, new data can be appended only by adding small files, which can lead to poor quality of query performance.

Databricks Advantages

Databricks helps overcome these problems with Delta Lake and Photon.

Delta Lake: A file-based, open-source storage format that runs on top of existing data lakes, it is compatible with Apache Spark and other processing engines and facilitates ACID transactions and handling of scalable metadata, unifying streaming and batch processing.

Delta Tables, based on Apache Parquet, is used by many organizations and is therefore interchangeable with other Parquet tables. Semi-structured and unstructured data can also be processed by Delta Tables, which makes data management easy by allowing versioning, reliability, time travel, and metadata management.

It ensures:

• ACID
• Handling of scalable data and metadata
• Audit history and time travel
• Enforcement and evolution of schema
• Supporting deletes, updates, and merges
• Unification of streaming and batch

Photon: The lakehouse paradigm is becoming de facto but creating the challenge of the underlying query execution engine unable to access and process structured and unstructured data. What is needed is an execution engine that has the performance of a data warehouse and is scalable like the data lakes.

Photon, the next-generation query engine on the Databricks Lakehouse Platform, fills this need. As it is compatible with Spark APIs, it provides a generic execution framework enabling efficient data processing. It lowers infrastructure costs while accelerating all use cases, including data ingestion, ETL, streaming, data science, and interactive queries. As it does not need code change or lock-in, just turn it on to get started.

Read more on how Indium can help you: Building Reliable Data Pipelines Using DataBricks’ Delta Live Tables

Databricks Architecture

The Databricks architecture facilitates cross-functional teams to collaborate securely by offering two main components: the control plane and the data plane. As a result, the data teams can run their processes on the data plane without worrying about the backend services, which are managed by the control plane component.

The control plane consists of backend services such as notebook commands and workspace-related configurations. These are encrypted at rest. The compute resources for notebooks, jobs, and classic SQL data warehouses reside on the data plane and are activated within the cloud environment.

For the cloud engineer, this architecture provides the following benefits:

Eliminate Data Silos

A unified approach eliminates the data silos and simplifies the modern data stack for a variety of uses. Being built on open source and open standards, it is flexible. Enabling a unified approach to data management, security, and governance improves efficiency and faster innovation.

Easy Adoption for A Variety of Use Cases

The only limit to using the Databricks architecture for different requirements of the team is whether the cluster in the private subnet has permission to access the destination. One way to enable it is using VPC peering between the VPCs or potentially using a transit gateway between the accounts.

Flexible Deployment

Databricks workspace deployment typically comes with two parts:

– The mandatory AWS resources

– The API that enables registering those resources in the control plane of Databricks

This empowers the cloud engineering team to deploy the AWS resources in a manner best suited to the business goals of the organization. The APIs facilitate access to the resources as needed.

Cloud Monitoring

The Databricks architecture also enables the extensive monitoring of the cloud resources. This helps cloud engineers track spending and network traffic from EC2 instances, register wrong API calls, monitor cloud performance, and maintain the integrity of the cloud environment. It also allows the use of popular tools such as Datadog and Amazon Cloudwatch for data monitoring.

Best Practices for Improved Databricks Management

Cloud engineers must plan the workspace layout well to optimize the use of the Lakehouse and enable scalability and manageability. Some of the best practices to improve performance include:

• Minimizing the number of top-level accounts and creating a workspace as needed to be compliant, enable isolation, or due to geographical constraints.
• The isolation strategy should ensure flexibility without being complex.
• Automate the cloud processes.
• Improve governance by creating a COE team.

Indium Software, a leading software solutions provider, can facilitate the implementation and management of Databricks Architecture in your organization based on your unique business needs. Our team has experience and expertise in Databricks technology as well as industry experience to customize solutions based on industry best practices.

FAQ

The post What Cloud Engineers Need to Know about Databricks Architecture and Workflows appeared first on Indium.

To get ahead of this, data wrangling was developed to get data in shape for automation. Here, we’ll look at what is meant by “data wrangling”, the stages involved, and why this process is so important to businesses.

What is Data Wrangling?

Data wrangling is the procedure taken for preparing raw data for rapid decision-making and analysis by analysts. It is a process that allows businesses to deal with data that are more complex in a shorter amount of time, which ultimately results in more accurate outcomes and improved judgments.

Methods of data wrangling are required to be utilized in a variety of settings and scenarios. Common use cases include the consolidation of several data sets into a single, unified dataset, as well as the identification and removal of gaps or spaces within data sets.

Steps Involved in Data Wrangling

Discovery

The first thing that has to be done is to become familiar with the raw data. Analyzing the data structures as well as its trends, locating any errors or ambiguities, and determining what aspects of the data may be removed are all necessary steps in getting the data suitable for use.

Structuring

After you have gained an understanding of what the raw data is and why you are collecting it, you can now begin organizing it. Among these tasks are the organization of data into rows and columns, the translation of images into text, and the creation of an archive-friendly file format.

Cleansing

There are always going to be some extreme occurrences in a dataset that might potentially distort the conclusions. You must clean up the data to get the most out of it. In the third phase, the data is rigorously cleaned to guarantee the greatest possible analytical quality. You will need to convert null values, eliminate duplication and strange characters, and standardize the layout if you want the data to be more uniform.

Data Enrichment

After you have finished Step 3, it is time to “enrich” your data by taking an inventory of what is already there and planning how to enhance it by adding new information. For instance, auto insurance companies might benefit from having access to local crime information to make more accurate risk assessments for their customers.

Validation

After collecting enough information, it’s time to validate it. Data consistency is ensured across your whole dataset by applying validation rules in iterative sequences. Data security and integrity can be guaranteed by following a set of validation criteria. This action mimics the reasoning behind the data normalization phase, another method for standardizing data through validation criteria.

Publishing

Analysts can disclose data when it has been thoroughly vetted and confirmed. The firm might disseminate it as a report or an electronic file. It may be used to make a database or refined into more complex data structures like a data warehouse.

Data analysts will occasionally make updates to their documented reasoning for transforming data. They will be able to make decisions on future projects much more quickly. Like chefs who keep a record of their transformation logic to save time, experienced data analysts and scientists do the same thing.

How Automation Helps Data Wrangling

We’ve covered some of the theories behind data wrangling. The importance of this procedure may be appreciated by considering the role that automation technologies play in facilitating the attainment of data-wrangling tasks.

Use of DataOps

The term “DataOps” refers to a collection of processes for managing data that, when implemented throughout an organization, make for more efficient data flows and consistent data consumption. It enhances the quality of the data and also the data structures, which speeds up processes and provides faster insights from the data, matching of the data, and security from beginning to finish.

Time Reduction

Data scientists can spend more of their time on modeling and analysis when they use tools that are automated throughout the data preparation process. These have the potential to significantly reduce the amount of time required to clean and validate data, hence allowing for more fruitful studies.

More In-depth Intelligence

The collection, organization, and analysis of data are fundamental to the successful operation of every facet of a business, from sales and marketing to accounting and finance. By utilizing data and doing manipulations on that data, you may be able to get insight into the present state of health of your business. With this knowledge, you will be able to direct your efforts to the areas of the organization that require them the most.

Prevents Data Leakage

When an algorithm for machine learning is given access to a data set that contains information that is not important to the task at hand, this can lead to data leakage. It is strongly encouraged to use data-wrangling automated tools to review and prepare data promptly. This will help to reduce the likelihood of this leakage occurring.

Faster Better Decision-making

The fast delivery of information is crucial to the making of sound business decisions. You can make the best decision in less time using automated tools for data wrangling and analytics. 

Conclusion

Cleaning, understanding, and analyzing raw data is impossible without data wrangling. As a result, useful information is gathered, new insights are developed, and company procedures may be altered or improved. There are several methods for performing data wrangling. If you want to save time and get the most out of the procedure, follow these best practices.

• Learn to read the signs in the data and use that knowledge to guide businesses to success.
• Gather relevant information.
• Determine what level of precision and accuracy is required for your data.
• To guarantee accuracy and cut down on waste, reevaluate the wrangled data

Indium Case Study:

For a leading Consulting Firm in the US, Indium helped the client to build the Data Analytics Platform (DAP) using Tableau to store, process and provide the data access layers to support Net Promoter Score (NPS) Next gen initiatives.

Indium designed a robust data model, implemented ingestion workflows, leveraged data tables and data pipelines and created dashboards using tools such as Amazon EC2, AWS S3, Postgres, Alteryx, Tableau.

The solution provided automated survey data extraction and storage in data lake, Data analysis and Insights by Product Categories and 10x efficiency in Dashboard Performance.

To know more about how Indium can help you with your data preparation needs, visit https://www.indiumsoftware.com/data-and-analytics/ or write to us at info@www.indiumsoftware.com. 

The post <strong>Essentials of Data Wrangling and How Automation Helps</strong> appeared first on Indium.

Smart Data Pipelines for Ever-Changing Business Needs

The world today is moving fast, and requirements changing constantly. Businesses need to respond in real-time to improve customer delight and become efficient to become more competitive and grow quickly. In 2020, the global pandemic further compelled businesses to invest in data and database technologies to be able to source and process not just structured data but unstructured as well to maximize opportunities. Getting a unified view of historical and current data became a challenge as they moved data to the cloud while retaining a part in on-premise systems. However, this is critical to understand opportunities and weaknesses and collaborate to optimize resource utilization at low costs.

The concept of the data pipeline is not new. Traditionally, data collection, flow, and delivery happened through batch processing, where data batches were moved from origin to destination in one go or periodically based on pre-determined schedules. While this is a stable system, the data is not processed in real-time and therefore becomes dated by the time it reaches the business user.

Check this out: Multi-Cloud Data Pipelines with Striim for Real-Time Data Streaming

Stream processing enables real-time access with real-time data movement. Data is collected continuously from sources such as change streams from a database or events from sensors and messaging systems. This facilitates informed decision-making using real-time business intelligence. When intelligence is built in for abstracting details and automating the process, it becomes a smart data pipeline. This can be set up easily and operates continuously without needing any intervention.

Some of the benefits of smart data pipelines are that they are:

● Fast to build and deploy

● Fault-tolerant

● Adaptive

● Self-healing

Smart Data Pipelines Based on DataOps Principles

The smart data pipelines are built on data engineering platforms using DataOps solutions. They remove the “how” aspect of data and focus on the 3Ws of What, Who, and Where. As a result, smart data pipelines enable the smooth and unhindered flow of data without needing constant intervention or building or being restricted to a single platform.

The two greatest benefits of smart data pipelines include:

● Instant Access: Business users can access data quickly by connecting the on-premise and cloud environments using modern data architecture.

● Instant Insights: With smart data pipelines, users can access streaming data in real-time to gain actionable insights and improve decisions making.

As the smart data pipelines are built on data engineering platforms, it allows:

● Designing and deploying data pipelines within hours instead of weeks or months

● Improving change management by building resiliency to the maximum extent possible

● Adopting new platforms by pointing to them to reduce the time taken from months to minutes

Smart Data Pipeline Features

Some of the key features of smart data pipelines include:

Data Integration in Real-time: Real-time data movement and built-in connectors to move data to distinct data targets become possible due to real-time integration in smart data pipelines to improve decision-making.

Location-Agnostic: Smart Data Pipelines bridge the gap between legacy systems and modern applications, holding the modern data architecture together by acting as the glue.

Streaming Data to build Applications: Building applications become faster using smart data pipelines that provide access to streaming data with SQL to get started quickly. This helps utilize machine learning and automation to develop cutting-edge solutions.

Scalability: Smart data integration using striim or data pipelines help scale up to meet data demands, thereby lowering data costs.

Reliability: Smart data pipelines ensure zero downtime while delivering all critical workflows reliably.

Schema Evolution: The schema of all the applications evolves along with the business, ensuring keeping pace with changes to the source database. Users can specify their preferred way to handle DDL changes.

Pipeline Monitoring: Built-in dashboards and monitoring help data customers monitor the data flows in real-time, assuring data freshness every time.

Data Decentralization and Decoupling from Applications: Decentralization of data allows different groups to access the analytical data products as needed for their use cases while minimizing disruptions to impact their workflows.

Benefit from indium’s partnership with striim for your data integration requirements: REAL-TIME DATA REPLICATION FROM ORACLE ON-PREM DATABASE TO GCP

Build Your Smart Data Pipeline with Indium

Indium Software is a name to reckon with in data engineering, DataOps, and Striim technologies. Our team of experts enables customers to create ‘instant experiences’ using real-time data integration. We provide end-to-end solutions for data engineering, from replication to building smart data pipelines aligned to the expected outcomes. This helps businesses maximize profits by leveraging data quickly and in real-time. Automation accelerates processing times, thus improving the competitiveness of the companies through timely responses.

The post <strong>Why You Should Use a Smart Data Pipeline for Data Integration of High-Volume Data</strong> appeared first on Indium.

This has led to the emergence of data mesh, a paradigm shift in modern data architecture that allows data to be considered a product. As a result, data architectures are being designed with distributed data around business domains with a focus on the quality of data being produced and shared with consumers.

Domain-Driven Design for Scalable Data Architecture

In the Domain Driven Design, or DDD, software design approach, the solution is divided such that the domains align with business capabilities, organizational boundaries, and software. This is a deviation from the traditional approach, where technologies are at the core of data architecture and not business domains.

Data mesh is a modern architectural pattern that can be built using a service such as AWS Lake Formation. The AWS modern data architecture allows architects and engineers to:

• Build scalable data lakes rapidly
• Leverage a broad and deep collection of purpose-built data services
• Be compliant by providing unified data access, governance, and security

Why You Need a Data Mesh

Businesses should be able to store structured and unstructured data at any scale, which can be available for different internal and external uses. Data lakes may require time and effort to ingest data and be unable to meet the varied and increasing business use cases. Often businesses try to cut costs and maximize value by planning one-time data ingestion into their data lake consuming it several times. But what they truly need is a scalable data lake architecture that scales. This adds value and provides continuous, real-time data insights to improve competitive advantage and accelerate growth.

By designing a data mesh on the AWS Cloud, businesses can experience the following benefits:

• Data sharing and consumption across multiple domains within the organization is simplified.
• Data producers can be onboarded at any time without the need for maintaining the entire data-sharing process. Data producers can continue with collecting, processing, storing, and onboarding data from their data domain into the data lake as and when needed.
• This can be done without incurring additional costs or management overhead.
• It assures security and consistency, thereby enabling external data producers also to be included and data shared with them in the data lake.
• Data insights can be gained continuously, in real-time, without disruptions

Features of AWS Data Architecture for Scalability

A data producer collects, processes, stores, and prepares for consumption. In the AWS ecosystem, the data is stored in Amazon Simple Storage Service (Amazon S3) buckets with multiple data layers if required. AWS services such as AWS Glue and Amazon EMR can be used for data processing.

AWS Lake Formation facilitates the data producer to share the processed data with the data consumer based on the business use cases. As the data produced grows, the number of consumers also increases. The earlier approach to managing this data-sharing manual is ineffective and prone to errors and delays. Developing an automated or semi-automated approach to share and manage data and access is an alternative, but also limited in effectiveness as it needs time and effort to design and build the solutions and also ensure security and governance. Over a period of time, it can become complicated and difficult to manage.

The data lake itself may become a bottleneck and not grow or scale. This will require redesigning and rebuilding the data lake to overcome the bottleneck and lead to increased utilization of cost, time, and resources.

This hurdle can be overcome using AWS Auto Scaling, which monitors applications and provides a predictable and cost-effective performance through automatic adjustment of capacity. It has a simple and powerful user interface that enables building plans for scaling resources such as Amazon ECS tasks, Amazon EC2 instances, Amazon DynamoDB tables and indexes, Amazon Aurora Replicas, and Spot Fleets. It provides recommendations for optimizing performance and costs. Users of Amazon EC2 Auto Scaling can combine it with AWS Auto Scaling to scale resources used by other AWS services as needed.

Benefits of AWS Auto Scaling

Some of the benefits of using AWS Auto Scaling include:

• Quick Setup for Scaling: A single, intuitive interface allows the setting of target utilization levels for different resources. A centralized control negates the need for navigating to other consoles.
• Improves Scaling Decisions: By building scaling plans using AWS Auto Scaling, businesses can automate the use of different resources by different groups based on demand. This helps with balancing and optimizing performance and costs. With AWS Auto Scaling, all scaling policies and targets can be created automatically based on need, adding or removing capacity in real time based on changes in demand.
• Automated Performance Management: AWS Auto Scaling helps to optimize application performance and availability, even in a dynamic environment with unpredictable and constantly changing workloads. By continuously monitoring applications, it ensures optimal performance of applications, increasing the capacity of constrained resources during a spike in demand to maintain the quality of service.
• Pay Per Use: Utilization and cost efficiencies of AWS services can be optimized as businesses pay only for the resources they need.

Indium to Enable Modern Data Architecture on AWS Ecosystem

Indium Software has demonstrated capabilities in AWS ecosystem, having delivered more than 250 data, ML, and DevOps solutions in the last 10+ years.

Our team consists of more than 370 data, ML, and DevOps consultants, 50+ AWS-certified engineers, and experienced technical leaders delivering solutions that break barriers to innovation. We work closely with our customers to deliver solutions based on the unique needs of the business.

FAQs

How can I scale AWS resources?

AWS offers different options for scaling resources.

• Amazon EC2 Auto Scaling ensures access to the correct number of Amazon EC2 instances for handling the application load.
• Application Auto Scaling API that allows defining scaling policies for automatic scaling of AWS resources. It also allows scheduling scaling actions on a one-time or recurring basis.
• AWS Auto Scaling facilitates the automatic scaling of multiple resources across multiple services.

What is a scaling plan?

The collection of instructions for scaling for different AWS resources is called a scaling plan. Two key parameters for this are resource utilization metric and incoming traffic metric.

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