Data downtime encompasses a wide range of problems related to data, such as it being partial, erroneous, inaccurate, having a few null values, or being completely outdated. Tackling the issues with data quality can take up the time of data scientists, delaying innovation and value addition.

Data analysts typically end up spending a large part of their day collecting, preparing and correcting data. This data needs to be vetted and validated for quality issues such as inaccuracy. Therefore, it becomes important to identify, troubleshoot, and fix data quality to ensure integrity and reliability. Since machine learning algorithms need large volumes of accurate, updated, and reliable data to train and deliver solutions, data downtime can impact the success of these projects severely. This can cost companies money and time, leading to revenue loss, inability to face competition, and unable to be sustainable in the long run. It can also lead to compliance issues, costing the company in litigation and penalties.

Challenges to Data Availability

Some common factors that cause data downtime include:

Server Failure: If the server storing the data fails, then the data will become unavailable.

Data Quality: Even if data is available but is inconsistent, incomplete, or redundant, it is as good as not being available.

Outdated Data: Legacy data may be of no use for purposes of ML training.

Failure of Storage: Sometimes, the physical storage device may fail, making data unavailable.

Network Failure: When the network through which the data is accessed fails, data can become unavailable.

Speed of Data Transfers: If the data transfer is slow due to factors such as where data is stored and where it is used, then that can also cause data downtime.

Compatibility of Data: If the data is not compatible with the environment, then it will not be available for training or running the algorithm.

Data Breaches: Access to data may be blocked, or data may be stolen or compromised by malicious factors such as ransomware, causing data loss

Best Practices for Preventing Data Downtime

Given the implications of data downtime on machine learning algorithms, business operations, and compliance, enterprises must ensure the quality of their data. Some of the best practices in avoiding data downtime include:

Create Data Clusters:As storage devices, networks, and systems can fail, creating clusters to spread data will improve availability in case of failures and prevent or minimize data loss. To enable responding to issues at the earliest, tracking and monitoring of availability is also important. The infrastructure should be designed for load balancing and resiliency in case of DDoS attacks.

Accelerate Recovery: Failures are inevitable, and therefore, being prepared for a quick recovery is essential. It could range from troubleshooting to hardware replacement or even restarting the operating systems and database services. It requires the right skills to match the technologies used to speed up the process.

Remove Corrupted Data: Incomplete, incorrect, outdated, or unavailable cause data corruption. Such data cannot be trusted and requires a systematic approach to identify and rectify the errors. The process should be automated and prevent new errors from being introduced.

Improve Data Formatting and Organization: Often, enterprises grapple with the issue of inaccurate data. It is difficult to access and use due to being formatted differently. Deploying tools that can integrate data onto a shared platform is important.

Plan for Redundancy and Backups: Back up data and store it in separate locations or distributed networks to ensure availability and faster restoration in case data is lost or corrupt. Setting up storage devices in a redundant array of independent disks (RAID) configuration is also one approach for this.

Use Tools to Prevent Data Loss: Data breaches and data center damages can be mitigated using data loss prevention tools.

Erasure Coding: In this data protection method, data is broken into fragments, expanded, and then encoded with redundant pieces of data. By storing them across different locations or storage devices, data can be reconstructed from the fragments stored in other locations even if one fails or data becomes corrupted.

Indium to Ensure Your Data Quality

Indium Software is a cutting-edge technology solutions provider with a specialization in data engineering, data analytics, and data management. Our team of experts can work with your data to ensure 24×7 availability using the most appropriate technologies and solutions.

We can design and build the right data architecture to ensure redundancy, backup, fast recovery, and high-quality data. We ensure resilience, integrity, and security, helping you focus on innovation and growth.

Our range of data services include:

Data Engineering Solutions to maximize data fluidity from source to destination BI & Data Modernization Solutions to facilitate data-backed decisions with insights and visualization

Data Analytics Solutions to support human decision-making in combination with powerful algorithms and techniques

AI/ML Solutions to draw far-reaching insights from your data.

FAQs

What is the difference between data quality and accuracy?

Data quality refers to data that includes the five elements of quality:

● Completeness

● Consistency

● Accuracy

● Time-stamped

● Meets standards

Data Accuracy is one of the elements of data quality and refers to the exactness of the information.

Why is data availability important for AI/ML?

Large volumes of high-quality, reliable data is needed to train artificial learning/machine learning algorithms. Data downtime will prevent access to the right kind of data to train algorithms and get the desired result.

The post Avoid Data Downtime and Improve Data Availability for AI/ML appeared first on Indium.

Some of the key technologies that enable the data lakehouse to provide these benefits include:

• Layers of metadata
• Improved SQL execution enabled by new query engine designs
• optimized access for data science and machine learning tools.

Data Lakes for Improved Performance

Metadata layers track the files that can be a part of different table versions to enable ACID-compliant transactions. They support streaming I/O without the need for message buses such as Kafka), facilitating accessing older versions of the table, enforcement and evolution of schema, and validating data.

But among these features, what makes the data lake popular is its performance with the introduction of new query engine designs for SQL analysis. In addition, some optimizations include:

• Hot data caching in RAM/SSDs
• Cluster co-accessed data layout optimization
• Statistics, indexes, and other such auxiliary data structures
• Vectorized execution on modern CPUs

This makes data lakehouse performance on large datasets comparable to other popular TPC-DS benchmark-based data warehouses. Being built on open data formats such as Parquet makes access to data easy for data scientists and machine learning engineers in the lakehouse.

Indium’s capabilities with Databricks services: UX Enhancement & Cross Platform Optimization of Healthcare Application

Easy Steps to Building Databricks Data Lakehouse on AWS

As businesses increase their adoption of AI and analytics and scale up, businesses can leverage Databricks consulting services to experience the benefits of their data by keeping it simple and accessible. Databricks provides a cost-effective solution through its pay-as-you-go solution on Databricks AWS to allow the use of existing AWS accounts and infrastructure.

Databricks on AWS is a collaborative workspace for machine learning, data science, and analytics, using the Lakehouse architecture to process large volumes of data and accelerate innovation. The Databricks Lakehouse Platform, forming the core of the AWS ecosystem, integrates easily and seamlessly with popular Data and AI services such as S3 buckets, Kinesis streams, Athena, Redshift, Glue, and QuickSight, among others.

Building a Databricks Lakehouse on AWS is very easy and involves:

Quick Setup: For customers with AWS partner privileges, setting up Databricks is as simple as subscribing to the service directly from their AWS account without creating a new account. The Databricks Marketplace listing is available in the AWS Marketplace and can be accessed through a simple search. A self-service Quickstart video is available to help businesses create their first workspace.

Smooth Onboarding: The Databricks pay-as-you-go service can be set up using AWS credentials. Databricks allows the account settings and roles in AWS to be preserved, accelerating the setting up and the kick-off of the Lakehouse building.

Pay Per Use: The Databricks on AWS is a cost-effective solution as the customers have to pay based on the use of resources. The billing is linked to their existing Enterprise Discount Program, enabling them to build a flexible and scalable lakehouse on AWS based on their needs.

Try Before Signing Up: AWS customers can opt for a free 14-day trial of Databricks before signing up for the subscription. The billing and payment can be consolidated under their already-present AWS management account.

Benefits of Databricks Lakehouse on AWS

Apart from a cost-effective, flexible and scalable solution for improved management of AI and analytics workloads, some of the other benefits include:

• Supporting AWS Graviton2-based Amazon Elastic Compute Cloud (Amazon EC2) instances for 3x improvement in performance
• Exceptional price-performance ensured by Graviton processors for workloads running in EC2
• Improved performance by using Photon, the new query engine from Databricks Our Engineering team ran benchmark tests and discovered that Graviton2-based

It might be interesting to read on End-To-End ML Pipeline using Pyspark and Databricks (Part I)

Indium–A Databricks Expert for Your AI/Analytics Needs

Indium Software is a leading provider of data engineering, machine learning, and data analytics solutions. An AWS partner, we have an experienced team of Databricks experts who can build Databricks Lakehouse on AWS quickly to help you manage your AI and analytics workloads better.

Our range of services includes: Data Engineering Solutions: Our quality engineering practices optimize data fluidity from origin to destination.

BI & Data Modernization Solutions: Improve decision making through deeper insights and customized, dynamic visualization

Data Analytics Solutions: Leverage powerful algorithms and techniques to augment decision-making with machines for exploratory scenarios

AI/ML Solutions: Draw deep insights using intelligent machine learning services

We use our cross-domain experience to design innovative solutions for your business, meeting your objectives and the need for accelerating growth, improving efficiency, and moving from strength to strength. Our team of capable data scientists and solution architects leverage modern technologies cost-effectively to optimize resources and meet strategic imperatives.

Inquire Now to know more about our Databricks on AWS capabilities.

The post Building a Databricks Lakehouse on AWS to Manage AI and Analytics Workloads Better appeared first on Indium.

However, this transition necessitates the creation of a detailed migration plan. What method will a corporation use to transfer data? How will the information be organised? How will a business be able to track data?

Setting goals and expectations for what the firm wants to achieve is the first step in migrating data to the cloud. For instance, how much data does the organisation intend to transfer, and at what point will the amount of data to be moved be reduced? Who will take over the project’s duties and responsibilities? What is the budget for the company, and what cloud platform will be used? The planning phase also includes code development and effective cloud migration strategies. The smoother the relocation process, the more well-planned it is.

Today’s organizations rely heavily on data for decision-making. These new-age data users, who aren’t only business intelligence experts, might make up the majority of an organization’s workforce, which is only growing. Considering this, the automated metadata management platform must be user-friendly in order to modernise data. As a result, the user interface must be so basic that users, regardless of their professional expertise, can readily access data.

Having discussed about the importance of data migration to the cloud for BI, let’s look at some of the data modernization trends.

Data Modernization Trends to look out for

Trend #1 – Uniform Implementation

When migrating to the cloud, the entire organisation must approach data in the same way. Business intelligence teams must teach data concepts, such as how to categorise, insert, and access data, so that the entire organisation can communicate in a common language. The analytics created from the data will be inconsistent if two departments use different categories for the same data. It’s also critical for data teams to teach the rest of the firm how to use and analyse data so that it may be put to the best possible use.

Effective data managers, on the other hand, strive to move the correct data to the right place as they transition their data. Rather than merely scaling up their existing data storage solution, data managers are increasingly attempting to turn the data into more valuable configurations. This shift does more than just storage. Data managers strive to improve analysis, interaction with other systems, and other aspects of data management. Instead of replicating local storage for 20 subsidiary offices, a company might construct global repositories based on data classification, such as worldwide marketing, operations records, logistic records etc

Trend #2 – Multi-functional adoption / Wider adoption

Markets that have previously recognised the value of data migration, such as banking, financial services, and insurance (BFSI), are expected to continue to employ it heavily. Similarly, marketing departments across sectors are expected to maintain their market leadership in data migration adoption and use to strengthen their data analysis capabilities.

However, as the benefits of data migration become more widely recognised, new businesses and professions are seeing the most rapid growth. Human resources is expected to be a fast-growing industry, according to Markets and Markets, as HR professionals seek data migration to integrate multiple data sources for improved data analysis.

Similarly, retail and consumer products are expected to expand the most, as companies strive to better understand customers throughout the product and customer life cycle. As the benefits of data migration and data analysis become more widely acknowledged, we can expect this shifting demand between industries and job functions to continue. While we may not be able to forecast which segments will expand the fastest next year, we can surely expect change to continue.

Trend #3 – Automated storage and storage scaling

Large organisations quickly exhaust the capacity of local hard drive arrays due to the general increase in data volume. Data managers must juggle frequent requests for more capacity while also staying ahead of hardware obsolescence and failure. Migrating data to the cloud will not totally solve these problems, but it will alleviate the urgency and complexity.

The hours of installation, configuration, testing, and troubleshooting necessary to install physical hard drives are readily replaced by mouse clicks on the cloud interface. Scaling storage is simple and perhaps automated, which appeals to data managers and drives the trend of moving more data to the cloud.

The largest corporations continue to exceed their current capacity. Markets & Markets predicts that demand for data migration in total dollars will continue to be dominated by these huge enterprises. However, as the cloud’s flexibility becomes more generally acknowledged in 2022, small and medium-sized businesses are likely to drive the fastest-growing section of the data migration market.

Trend #4 – Capitalizing on Integrational features

Companies that are using cloud for their applications are paying close attention to the integration offerings of cloud vendors. For example, Azure’s integration capabilities are becoming increasingly popular among Azure platform customers, and some organisations have begun to build lightweight integration frameworks on top of AWS’ offerings.

Rapid change will always have an impact on technology; it’s the essence of the business. Still, there’s no disputing that we’re in the midst of a generational transformation, with businesses pivoting faster than ever to stay ahead of the pack. Although digital integration is not new, its impact on a company’s long-term performance and importance has never been greater.

Trend #5 – Data restructuring and refined data transfer

Previous data migrations may have merely shifted data from one repository to another, but with AI, data migration cycles are more optimized. As such, data analysis becomes more efficient and effective. Data modernization, which entails migrating data from legacy databases to modern databases, is driven by this need. Unstructured data is categorised and structured, and old databases are restructured and refined, thanks to data modernization. According to a recent Deloitte poll, 84 percent of respondents have begun data transformation initiatives, with the financial services industry leading the way.

Companies who were among the first to migrate data to the cloud are now actively involved in data migration for data modernization. This contributes to the established industries’ status as market leaders in data migration. Marketing departments, for example, may have been the first to migrate data to the cloud for data analysis, but they are now also leading the way in data transfer from one database to another. Thus they are seeking the benefits of scalability, cost savings, flexibility, performance, and extended capability.

This might interest you: 5 Tips For Successful Data Modernization

Wind-up

We recognise that adopting modern technologies may help firms remain competitive and nimble as they seek out flexible, scalable data infrastructure solutions. Those who have yet to modernise, on the other hand, will face an uphill battle in extracting insights from their data. According to a recent IDG poll, more than 90% of data professionals claimed it is difficult to make data available in a format that can be used for analytics.

Companies should continue to improve their data architecture and keep an eye on industry trends and cutting-edge technology to help them compete with data insights. On-premises and legacy tools, on the other hand, were never designed to handle enormous amounts of data in a flexible and scalable manner. As new trends drive significant changes in data management strategy, businesses must prioritise modernization.

The post Data Modernization Trends: Keep these in mind appeared first on Indium.

Indeed, data is the most valuable, enduring asset of any organization, providing the foundation for digital transformation and strategy.

Effective data management is an essential part of today’s unpredictable business environment. Managing and understanding data better can help companies make informed and profitable business decisions.

The total volume of data that organizations across the world create, capture, and consume is forecast to reach 59 zettabytes in 2021, according to Statista. This data does not only comprise structured data in the form of documents, PDFs, and spreadsheets, it also includes tweets, videos, blog articles and more that make up unstructured data, which is essentially eclipsing the volume of structured data. Therefore, organizations not only face storage challenges but have a significant challenge in processing the wide-ranging data types.

Data Modernization

The process of migrating siloed data to modern cloud-based databases or lakes from legacy databases is known as data modernization. It enables organizations to be agile and eliminate bottlenecks, inefficiencies, and complexities of legacy systems.

A modernized data platform helps in efficient data migration, faster ingestion, self-service discovery, near real-time analytics and more key benefits.

For any modern business focused on building and updating the data architecture to spruce up their data core, data modernization is not only important but essential.

To gain optimal value, accelerate operations and minimize capital expenditure, companies must build and manage a modern, scalable data platform. Equally, it is vital to identify and deploy frameworks of data solutions along with data governance and privacy methodologies.

Data modernization is not without challenges as it requires creating a strategy and robust methods to access, integrate, clean, store, and prepare data.

Tips For Successful Data Modernization

Data modernization is critical for any modern business to stay ahead of the curve. With that said, let us find out how companies can be successful in their data modernization efforts.

Revise Current Data Management Strategy And Architecture

It is important to have an in-depth understanding of the organization’s business goals, data requirements and data analytics objectives when a company starts modernizing.

Thereafter, a data management architecture can be designed to integrate existing data management systems and tools, while innovative methods and models can be leveraged to accomplish the organization’s immediate objectives and adapt to future needs.

A well-designed architecture will enable data modernization to be approached systematically and holistically, thereby eliminating data silos and compatibility issues. It will also deliver consistent value and be flexible to integrate new capabilities and enhancements.

Inventory And Mapping Of Data Assets

If an organization cannot identify where the data assets are and what is protecting them, it will be tough to know if the access provided is suitably limited or widely available to the internet.

It is essential for organizations to first understand what data is being collected, what is being collected and what is being sent out. This helps identify the requirements and how a modern data management technology can help simplify the company’s data and analytics operating model.

The best way to begin a meaningful transformation is to simplify the problem statement. Hybrid cloud is also an integral part of any modern data management strategy.

Data Democratization A Core Objective

Until a few years ago, organizations had one major reason to modernize their data management ecosystems—which was to manage their rapidly growing data volumes.

Today the single, overriding reason is data democratization, which is about getting the right data at the right time to the right people.

It gives organizations wide-ranging abilities such as implementing self-service analytics, deploying large data science and data engineering teams, building data exchanges and zones for collaboration with trading partners and go after more data management activities.

Another key advantage of democratizing data is it helps companies achieve data trust and affords them more freedom to concentrate on transformative business outcomes and business value.

Robust governance is another focus area for organizations, who can thereby reduce data preparation time and give data scientists and other business issues the time to focus on analysis.

Technology Investment

Continuous investment in master governance and data management technologies is the best way to gain maximum control over organizational data.

Assuming ownership of data elements and processes, with leadership support, can often be ignored in data management programs but they are a key enabler in managing complex environments.

It is important for chief information officers (CIOs) to take stock of the legacy technologies present on-premises, the decision support system that is ageing and will be out of contract in a few months and more contribute to data modernization projects being successful.

Data Accountability

Establishing data accountability is a basic yet crucial step in reimagining data governance. Organizations that go beyond process and policy and prioritize insights and quality measures tend to be the most successful when it comes to data modernization.

In today’s rapidly changing world, almost everything is connected and digital. In this scenario, every bit of data about customers, transactions and internal processes are business assets that can be mined to enhance customer experience and improve the product.

Among the key issues facing IT leaders is while digital points continue to increase rapidly, many remain locked to monolithic legacy systems. A holistic look at solution development and delivery that leverage Agile, DevOps, Cloud and more such approaches are essential.

Summary

It is important for organizations to be aware of the evolving data management methods and practices. It could be said that data management is one of the most demanding issues IT leaders are likely to encounter in the year 2021 and beyond. For a company’s data modernization process to be successful, their data management approach should align with their overall business strategy.

The post 5 Tips For Successful Data Modernization appeared first on Indium.

Cloud, IoT, AI/ML and so on are no more mere buzzwords but present in several aspects of your business’ operations. Marketsandmarkets.com estimates the global digital transformation market size to be USD 469.8 billion in 2020 and expects it to grow at a Compound Annual Growth Rate (CAGR) of 16.5% by 2025 to touch USD 1009.8 billion.

However, what these numbers do not reveal is that many of the digitization efforts are small projects and do not span enterprise-wide processes.

According to a Gartner report, digital transformation should have data and analytics at its core, but almost 50% of corporate strategies do not include this. A McKinsey report points out that to leverage cloud, businesses moving from traditional monolithic and configured for fixed/static capacity, called data modernization, cannot expect to enjoy the dynamism of cloud by merely shifting their data to a few data centers.

Another McKinsey report clearly identifies streaming data as the fundamental component that will drive data modernization and streaming integration platforms as being critical to successful digital transformation.

The streaming analytics market is expected to grow at a Compound Annual Growth Rate (CAGR) of 25.2% from USD 12.5 billion to USD 38.6 billion between 2020 and 2025, according to Marketsandmarkets.com. The report also identifies inadequate system integrity as a bottleneck to the growth of this industry.

Data Modernization with Business Agility

While moving data from legacy systems to the modern cloud is an important step in data modernization, equally important is the need to be able to tap data in real time.

Technologies today have leveled the field to some extent by bringing Big Data within reach of organizations of all sizes, across industries and located anywhere in the world.

Speed is the critical differentiator here as any delay in tapping opportunities is equivalent to handing the game to the competitor.

Access to real-time connected data and the ability to create dynamic dashboards to draw insights for making informed decisions has become essential for survival and growth. 

This needs constant data ingestion and the streaming data to be integrated with the enterprise data for further analytics and intelligence.  Stream data integration is key to making data modernization efforts bear fruit.

It can help identify trends and relationships, events and threats, customer behaviors and the opportunities they present, to identify ways to enhance the efficiency and efficacy of operations and minimize risks.

Some of the key areas where stream data integration can be beneficial include:

• Fraud detection
• Supply chain optimization
• Customer service
• Scheduling resource
• Dynamic pricing
• Preventive maintenance
• High-availability of IT systems and services

Striim Stream Data Integration

One of the most popular solutions for stream data integration is Striim, an end-to-end, enterprise-grade platform that enables easy integration of structured, semi-structured and unstructured data with sub-second latency on cloud or on-premises.

A non-intrusive, real-time change data capture solution, it enables in-flight processing and visualization of data with pre-built data pipelines.

Its wizard-based user interface and SQL-like language make it an ideal tool for business analysts and developers.

It facilitates the integration of data across Cloud, Big Data, and IoT devices without being bound to a single topology.

By continuously running queries to filter, transform, aggregate, enrich, and analyze the data-in-motion, it can deliver the output to any target with sub-second latency. It can also do batch processing of data.

With Striim’s Stream Data Integration you can:

• Provide real-time, consistent data of different types including JSON, delimited, XML, binary, free text, and change records to analytical and transactional systems using non-intrusive change data capture (CDC)
• Get an organization-wide view of all your business data from different sources such as databases, log files, sensors, and messaging systems
• Wizards-based development and pre-built integration accelerates the building of streaming data pipelines
• Improve operational decision making with timely insights

It is an enterprise-grade patented tool that ensures security, scalability and reliability of the data, delivering the data as per user requirement in the correct format for high-value operational workloads whenever it is needed.

Indium for Striim Implementation

Indium Software is a two-decade-old IT solutions provider working with cutting-edge technologies to power businesses and set them on a growth trajectory.

It has a dedicated team of Big Data experts with cross-domain experience. Indium enables businesses embarking on the data modernization journey to leverage Striim stream data integration platform and improve the quality and speed of their decision-making by drawing insights from real-time data.

To find out how your data modernization efforts can benefit from streaming data integration, contact us now.

The post Why Streaming Integration is Key to your Data Modernization Efforts appeared first on Indium.