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.

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.