What is Data Management?

Data management is the process of collecting, storing, organizing, maintaining, using, and disposing of data in an efficient and secure manner. It involves establishing policies and procedures for data acquisition, validation, storage, backup and recovery, access, and destruction. Effective data management is crucial to improve operations, and support growth.

Why Should You Adopt a Modern Approach to Data Management?

One of the main forces driving contemporary data management is the growth of big data. Businesses must come up with new methods to store, handle, and analyse data given its exponential increase. With its scalable and adaptable features, cloud computing offers the perfect option.

Increased Business Agility

The processing and analysis of data by organisations has been revolutionised by artificial intelligence (AI) and machine learning (ML). Businesses may use AI and ML to automate complicated data management activities, find patterns and insights, and make decisions more quickly and accurately. Businesses may confidently make data-driven decisions since ML systems can learn from past data to increase their accuracy over time enabling organizations to respond to changing business conditions more effectively..

Improved Data Analytics

Modern data management has increasingly reliant on data analytics and visualisation tools. These tools make it simpler for companies to recognise patterns, trends, and anomalies by transforming raw data into insights that can be used to gain valuable insights from their data.

Data Integration and Interoperability

A modern approach to data management supports the integration of data from multiple sources and systems. The increasing amount of data generated by various sources; data integration has become more challenging. Modern data management solutions must support data integration across multiple platforms and systems, providing a comprehensive view thus enabling organizations to more effectively leverage their data.

Governance with Regulations

Data governance refers to the policies, procedures, and controls used to manage data quality, security, and privacy. With the increasing importance of data in business decision-making, data governance has become critical to ensure data accuracy, security, and compliance with regulatory requirements. A modern approach to data management helps organizations comply with data privacy regulations, such as GDPR and HIPAA, and reduces the risk of penalties for non-compliance.

Also read: Crucial Role that Data Fabric Plays in Data Management Today

What should be considered for a successful approach to modern data management?

Having a successful approach to modern data management requires several key elements, including:

Effective data governance aids for data management to be successful, it is essential to implement clear policies and procedures for data gathering, validation, storage, backup, recovery, access, and deletion. Making sure that data is always correct, comprehensive, and consistent is essential for making well-informed decisions and running a successful organisation, so it is important to have a data quality management system in place.

The key to ensuring that data management operations are successful, efficient, and complement the overall business plan is having devoted, well-trained data management staff. To meet the organization’s changing demands, data management should be a process that is continually evaluated and improved.

Data management can present several challenges, including:

Data Volume: The exponential growth of data, also known as big data, can present a challenge for organizations that need to store, process, and analyze large amounts of data.

Data Variety: The increasing variety of data types and formats can make it difficult for organizations to manage and integrate data from multiple sources.

Data Quality: Ensuring the accuracy, completeness, and consistency of data can be challenging, especially as data is sourced from multiple systems and platforms.

Data Security: Protecting sensitive data from unauthorized access and ensuring compliance with data privacy regulations, such as GDPR and HIPAA, can be challenging.

Data Integration: Integrating data from multiple sources and systems can be difficult, especially when data formats are incompatible or data is stored in silos.

Data Governance: Implementing effective data governance policies and procedures that ensure consistent and compliant data management practices can be challenging.

Data Management Teams: Finding and retaining skilled data management professionals can be difficult, especially as the demand for these skills continues to grow.

Budget and Resources: Securing sufficient budget and resources to implement and maintain a robust data management program can be a challenge.

These challenges highlight the importance of adopting a comprehensive, well-planned, and well-executed data management strategy that takes into account the unique needs and requirements of each organization.

How Do You Create a Modern Data Architecture?

To create modern data architecture, you can follow these steps:

Step 1:  Start by defining the business requirements and determining what information the organisation needs to gather, store, and analyse in order to achieve its objectives. List the numerous data sources, including social media, transactional systems, logs, and third-party APIs. decide what modifications are required to accommodate the new design, and evaluate the existing data infrastructure.

Step 2:  Choose the best technology for data storage, processing, and analysis based on your needs and the sources of your data. Data lakes, data warehouses, and cloud services may all fall under this category. Then Design the data architecture in accordance with the needs, data sources, and technologies you have chosen. Creating data models, data pipelines, and data access patterns may be part of this. finally Implement the data architecture, then test it to see if it functions as expected and satisfies the criteria.

Step 3: Maintain regular monitoring of the data architecture and make changes as necessary to make sure it continues to fulfil the demands of the enterprise. Keep in mind that a modern data architecture needs to be scalable, versatile, and secure to suit the business’s continuously changing needs.

Conclusion

By leveraging the latest technologies and tools, and by having dedicated and well-trained data management teams in place, organizations can ensure that they have the right data at the right time to support their business needs. By adopting a modern approach to data management, organizations can increase operational efficiency, improve customer understanding, and gain a competitive advantage in their respective markets.

In conclusion, modern data management is essential for business success as it enables organizations to effectively collect, store, and analyze data to support informed decision making and drive business growth. The volume, variety, and velocity of data continues to increase, making it more important than ever for organizations to adopt modern data management practices that support effective data governance, security, and privacy.

The post Why Modern Data Management is Essential for Business Success 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.

L.L. Bean turned to Google Cloud to fulfill its cloud requirements. By modernizing data on, it experienced faster page loads and it was able to access transaction histories more easily. It also focused on value addition instead of infrastructure management. And, it reduced release cycles and rapidly delivered cross-channel services. These collectively improved its overall delivery of agile, cutting-edge customer experience.

Data Modernization with Google Cloud for Success

Many businesses that rely on siloed data find it challenging to make fully informed business decisions, and in turn accelerate growth. They need a unified view of data to be able to draw actionable, meaningful insights that can help them make fact-based decisions that improve operational efficiency, deliver improved services, and identify growth opportunities. In fact, businesses don’t just need unified data. They need quality data that can be stored, managed, scaled and accessed easily.

Google Cloud Platform empowers businesses with flexible and scalable data storage solutions. Some of its tools and features that enable this include:

BigQuery

This is a cost-effective, serverless, and highly scalable multi-cloud data warehouse that provides businesses with agility.

Vertex AI

This enables businesses to build, deploy, and scale ML models on a unified AI platform using pre-trained and custom tooling.

Why should businesses modernize with Google Cloud?

It provides faster time to value with serverless analytics, it lowers TCO (Total Cost of Ownership) by up to 52%, and it ensures data is secure and compliant.

Read this informative post on Cloud Cost Optimization for Better ROI.

Google Cloud Features

Improved Data Management

BigQuery, the serverless data warehouse from Google Cloud Platform (GCP), makes managing, provisioning, and dimensioning infrastructure easier. This frees up resources to focus on the quality of decision-making, operations, products, and services.

Improved Scalability

Storage and computing are decoupled in BigQuery, which improves availability and scalability, and makes it cost-efficient.

Analytics and BI

GCP also improves website analytics by integrating with other GCP and Google products. This helps businesses get a better understanding of the customer’s behavior and journey. The BI Engine packaged with BigQuery provides users with several data visualization tools, speeds up responses to queries, simplifies architecture, and enables smart tuning.

Data Lakes and Data Marts

GCP’s enables ingestion of batch and stream/real-time data, change data capture, landing zone, and raw data to meet other data needs of businesses.

Data Pipelines

GCP tools such as Dataflow, Dataform, BigQuery Engine, Dataproc, DataFusion, and Dataprep help create and manage even complex data pipelines.

Discover how Indium assisted a manufacturing company with data migration and ERP data pipeline automation using Pyspark.

Data Orchestration

For data orchestration too, GCP’s managed or serverless tools minimize infrastructure, configuration, and operational overheads. Workflows is a popular tool for simple workloads while Cloud Composer can be used for more complex workloads.

Data Governance

Google enables data governance, security, and compliance with tools such as Data Catalog, that facilitates data discoverability, metadata management, and data class-level controls. This helps separate sensitive and other data within containers. Data Loss Prevention and Identity Access Management are some of the other trusted tools.

Data Visualization

Google Cloud Platform provides two fully managed tools for data visualization, Data Studio and Looker. Data Studio is free and transforms data into easy-to-read and share, informative, and customizable dashboards and reports. Looker is flexible and scalable and can handle large data and query volumes.

ML/AI

Google Cloud Platform leverages Google’s expertise in ML/AI and provides Managed APIs, BigQuery ML, and Vertex AI. Managed APIs enable solving common ML problems without having to train a new model or even having technical skills. Using BigQuery, models can be built and deployed based on SQL language. Vertex AI, as already seen, enables the management of the ML product lifecycle.

Indium to Modernize Your Data Platform With GCP

Indium Software is a recognized data and cloud solution provider with cross domain expertise and experience. Our range of services includes data and app modernization, data analytics, and digital transformation across the various cloud platforms such as Amazon Web Server, Azure, Google Cloud. We work closely with our customers to understand their modernization needs and align them with business goals to improve the outcomes for faster growth, better insights, and enhanced operational efficiency.

FAQs

The post Data Modernization with Google Cloud 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.

Cloud cost optimization ensures that you spend the lowest possible amount on cloud resources while getting maximum value. It’s the best way to earn maximum ROI from your business’ cloud-based products or services.

This article will explore all the best practices and strategies you can apply for effective cloud cost optimization.

12 Best Strategies For Cloud Cost Optimization

There are many cloud optimization strategies companies use to ensure maximum efficiency. We will be talking about the 12 best ways to do so below:

1. Make a Good Budget

You can control how much money is spent by letting everyone know the goals and budget available for each project in a particular period. Always be specific with your budgeting, and don’t pick just any number. Instead, speak with your different heads of departments and stakeholders to know the budget required for each product. It is possible to set budget alerts on AWS/Azure/GCP which will keep the admin informed of breaches

2. Limit the Data Transfer Fees

All cloud providers charge egress fees for data transfer from their platforms and even within (between regions). You want to keep this minimal with proper planning and architecting. To get better pricing, you can check the fees the cloud providers such as AWS, GCP, and Azure charge for data transfer to pick the one that is most efficient while being the least expensive, especially if your operations are data transfer intensive.

3. Revisit Billing Information

AWS, Azure, and GCP have billing details on their respective portals that explain the cost details of each of their cloud services intricately. You can use these reports every quarter or monthly to review your cloud costs regularly and identify any redundant costs or services running.

4. Use Proper Sizing for Your Services

One of the most important processes before putting your workloads on the cloud is sizing them for the right services. You do not want to use larger specifications for workloads that require much smaller resources. This is an ongoing process that you can adapt to the growth of your business or needs. It can be extremely difficult to manually change the sizes of instances because many things are involved, such as the graphics, storage capacity, memory, database, combinations, etc.

5. Take Advantage of Spot Instances

On AWS, Spot instances are leftover resources at low prices, which customers typically bid for to run their jobs. You would typically use spot instances for short-term or batch jobs because they are unreliable. Azure and GCP also offer similar services on their cloud platforms: low-priority VMs and preemptive VMs. Using these resources can save you a lot of money, especially when used judiciously.

6. Do Routine Audits of Your Environment

Checking your cloud resources regularly can help you identify resources that are no longer in use but continue to cost you money. Idle resources are another thing cloud service providers like AWS, Azure, and GCP charge for even when you aren’t using them. By finding and merging the resources or dropping the ones that are not needed, you can optimize the cost of your cloud. You should be aware of your costs throughout the software development cycle below.

• Planning: You should budget your spending and make forecasts.

• Deployment and Operational Processes: Your team should look out for any spending changes and be ready to adjust accordingly.

• Design and Build: When your team is developing the architecture, data is key and informs the decisions.

• Monitoring: Once you have deployed your service, it is important to reassess by feature, product, or team and generate reports.

7. Architect According to Your Workload

AWS, Azure, and GCP have similar services, and depending on your systems, a multi-cloud approach may work for you. Multi-cloud deployment doesn’t limit you to being entirely dependent on one vendor; you can benefit from all their advantages. However, this may be expensive. With a single-cloud deployment, you can take advantage of discounts to make large purchases. Switching between cloud platforms is usually a problem and requires more training from the personnel. Examine both the single and multi-cloud environment by checking the following details across platforms:

• Cost per cloud service: Check the costs associated with storage, computing, database, etc.

• Time to Market: How long it takes you to deploy on each platform

• Skills: Examine the skills of your staff and which cloud provider they are most skilled in

• Available Regions: Do the cloud providers have regions close to you that could reduce latency

8. Be Attentive to Cost Irregularities.

When using AWS, you can use the cost management console to make budgets, optimize the cloud and predict the AWS cost without putting in more effort. Most cloud services, including Azure and GCP, now have a way to detect irregularities in your workloads. The system is customizable and allows you to set different thresholds for the notification warning. Finding the irregularity in question and stopping it from acquiring any extra cost is usually taken care of.

9. Use the Right Storage Options

The different cloud providers provide multiple storage options and picking the right one can help you save a lot of money. Amazon’s S3 storage system is one of the most used cloud storage platforms. Use the storage tiers depending on how frequently you access the data to get cost savings. You can use S3 with AWS or other services; it is incredibly user-friendly and has limitless storage.

10. Employ Reserved Instances

Reserved instances are computed instances you pay for on a 1-year or 3-year contract term. These instances can offer massive discounts on your cloud costs compared to on-demand instances. Azure and GCP have similar offerings where you can pick the instance you want, the region, and the specification. Reserved instances can offer discounts as high as 75%.

11. Find and Reduce Costs from Software Licenses

Software licenses are a very major and expensive part of cloud operations. Managing a manual license can be quite challenging and raises the risk of paying for software licenses that aren’t being used (CAPEX costs). This is why the cloud approach of operational costs (OPEX) is preferred because you only pay for what you use, and you do not have to tie your money down.

12. Leverage Cloud-Native Design

Optimize your workloads for the cloud to take full benefits and drive down your costs. The cloud has many efficient and low-cost tools across all the cloud providers. Leverage these tools with documentation to get the best recommendations for cloud architectures. AWS has extensive guides and professionals that can help you design the cloud system you need to help you reduce costs using the cloud’s basic principles.

Conclusion

If you follow all the practices above, you will see your cloud costs reduce in no time. It isn’t an easy transition as it involves every member of your team consciously considering and putting in efforts to reduce overall cost.

Indium Software help businesses save costs significantly with Cloud Optimization services. Our cloud optimization solutions deliver complete visibility across public cloud infrastructures and provide continuous optimization which is key to cost management.

.

The post Cost Optimization on Cloud for Better ROI appeared first on Indium.