Sprinkle Data

Top 30 Data Analytics Tools

Data is the new oil. It has become a treasured commodity today for data analytics and has taken on a serious status. With the daily growing data volume, it is now at a scale that no human can deal with the amount manually. Businesses worldwide have found growth in their organizations by incorporating data analytics into their existing technology platforms.

The concept of data analytics has evolved over time and will continue to rise. Data analytics has become an important part of managing a business today, where every business owner wants their business to grow and increase its revenue in order to maintain a competitive edge in this ever-changing marketplace, they need to be able to use data effectively.

What is Data Analytics?

Data analytics is the science of studying raw data with the intent of drawing conclusions from it. It is used in multiple industries to allow companies and organizations to make more promising data-driven business decisions.

Data analytics covers an entire spectrum of data usage, from collection to analysis to reporting. Understanding the process of data analytics is the ultimate power and it will be the future of almost every industry.

There are multiple types of data analytics including descriptive, diagnostic, predictive, and prescriptive analytics.

Let’s learn about the different types of data analytics in detail.

‍Types of Data Analytics:

Descriptive Data Analytics:

Descriptive data analytics is the process of examining data to summarize what is actually happening. It provides a basic understanding of how the business operates and helps to identify which factors are affecting the business and which aren't. It supports the exploration and discovery of insights from your existing data and based on that provides a basic understanding of your business.

Diagnostic Data Analytics:

Diagnostic Data Analytics is used to diagnose any business problems. It generally answers the question: why did it happen? Data can be examined manually, or used by an automated system to generate a warning. Diagnostic data analytics is an advanced analytical approach that is used to find the cause of the problem faced by a business.

Predictive Data Analytics:

Predictive data analytics is a form of analytics that uses both new and historical data to forecast activities, behavior, and trends. It is used to analyze current data to make predictions about future events. One important use case for predictive analysis is to help retailers understand customer buying patterns and optimize inventory levels to maximize revenues.

Prescriptive Data Analytics:

Prescriptive data analytics is the last level of analytics that is performed on the outcome of other types of analytics. It is the process of defining an action based on descriptive and predictive data analytics results. In this stage, different scenarios are analyzed to determine how each scenario is likely to play out given past data. This can help businesses know what action to take for a good outcome.

These four types of data analysis techniques can help you find hidden patterns in your data and make sense of it. All these types of data analytics are important in other ways and can be used in different business scenarios.

Importance of Data Analytics:

Data analytics is extremely important for any enterprise and has become a crucial part of every organization's strategy in the past decade. The reason for this is simple: Big data has opened up a world of opportunities for businesses. Data analysts have become essential in helping companies process their huge sets of data for making meaningful decisions.

The benefits offered by analyzing data are numerous some of them are mentioned below:

It helps businesses to determine hidden trends and patterns.

Improves efficiency and productivity of the business by helping them to take data-driven decisions.

Identifies weaknesses and strengths in the current approach.

Enhances decision-making, which helps businesses to boost their revenue and helps solve business problems.

It helps to perform customer behavior analysis accurately to increase customer satisfaction

Data analytics lets you know what is working and what can improve. According to experts, the lack of data analysis and usage can result in failed business strategies and also cause loss of customers. So in order to take your business to the next level, one must always adopt data analytics techniques and should be familiar with the steps involved in it.

Data Analysis Process: Steps involved in Data Analytics

Steps in data analytics are a set of actions that can be performed to create useful and functional data. In this section, we will detail the stages involved in data analytics.

Understanding Business Requirements

One of the most important factors behind successful data analysis is a proper understanding of the business requirements. An analyst needs to have a clear idea about what kind of problem the business is facing and what can be done to overcome the problem. The other important task is to understand what type of data needs to be collected to solve the given problem.

Collecting Data

When it comes to data analytics, it is very important that the right kind of data is collected. After understanding the business problem the analyst should be aware of the type of data to be collected to solve the problem. Data can be collected in many ways, including survey forms, interviews, market research, web crawlers, log files, event log files, and even through social media monitoring apps.

Data wrangling

In data wrangling, data is cleaned and managed so that it can be utilized in order to perform data analysis. This process can involve converting data from one format to another, filtering out invalid or incorrect data, and transforming data so that it can be more easily analyzed. Data wrangling is an important step in data analysis because it can help ensure that the data used in the analysis is of high quality and is in a suitable format.

There are many steps involved in data wrangling, including

1. Gathering data from a variety of sources.

2. Cleaning and standardizing the data.

3. Exploring the data to identify patterns and relationships.

4. Transforming the data into a format that can be used for different tasks.

5. Saving the wrangled data in a format that can be easily accessed and used in the future.

The steps involved in data wrangling can vary depending on the type and format of data you are working with, but the final goal is always the same, to transform raw data into a format that is more useful for performing accurate analysis.

Exploratory Data Analysis (EDA):

Exploratory Data Analysis (EDA) is a statistical approach used to achieve insights into data by summarizing its major features. This procedure is used to comprehend the data’s distribution, outliers, trends, and other factors. EDA can be used to select the best-fitting statistical models and input variables for a dataset.

A typical EDA process might begin with a series of questions, such as

What are the primary components of the dataset?

What are the most significant variables?

Are there any outliers or unusual observations or behaviors?

After asking these basic questions, the analyst should then investigate the data visually, using charts such as histograms, scatter plots, and box plots. These visual methods can help to identify features such as trends, and unusual observations. This process of EDA can help to reveal important insights into the data, and can be used to guide further analysis.

EDA can provide insights that may not be obvious from merely looking at the data itself. Overall, it is an essential tool for data analysis and should be used whenever possible.

Communicating Results:

Communicating results is the final and the most vital aspect of the data analysis life cycle because it allows others to understand the conclusions of the analysis. Results also need to be communicated in a clear and concise way so they can be easily understood by people with less technical acumen as well. Additionally, conveying results allows for feedback and discussion to improve the quality of the findings during the analysis procedure.

The data analytics life cycle generally goes through these five-step procedures that help to find precise conclusions. But apart from the benefits, some challenges are faced during the data analytics process.

Overall Challenges in Data Analysis:

There can be many types of challenges encountered during the data analysis journey but the two most common challenges are mentioned below:

Data issues

Data analysis-related issues.

1. Data Issues:

Data-related problems are one such type of issue encountered during the data analysis journey. Some data-related issues are mentioned below:

Incorrect or inaccurate data

Incomplete data

Data that is not timely ingested

Unorganized data

Irrelevant data

Data integration issues

Handling large datasets

The data team needs to guarantee to provide the correct data and a good and reliable data integration platform should be preferred to ensure correct and timely ingestion of data. A proper ETL tool that provides safe and secure data storage should be selected.

2. Data Analysis Related Issues:

The data analysis process can be challenging if the data is not well-organized, some challenges are mentioned below:

Absence of skills to interpret data.

Data cleaning and preparation can be very time-consuming.

Choosing the right statistical method can be a challenge.

The results of the analysis can be misinterpreted.

Communicating the results in a simpler way can be tough

To overcome these challenges businesses should use low-code data analytics platforms that will help to save manpower and thus reduce costs. With careful planning and execution, one can easily perform analysis without any hassles. By using the right tools and techniques, businesses can overcome these challenges and make better data-driven decisions.

Need for Data Analysis Tools:

In a world where data is continuously being generated, it is becoming hard to make sense of it all without the help of data analysis tools.

There are many reasons why we need data analysis tools. They help us to process, understand, and make use of data effectively. Data analysis tools help us to see patterns and trends in data without actually coding. Nowadays, businesses don't need a highly skilled person to perform the data analysis process in fact they can perform the analysis on their own because of the tools present in the market.

The data analysis tools in the market can also help to enhance communication and collaboration within your organization through alerts and email functionalities. In some cases, they can also help to automate decision-making processes.

Criteria For Choosing the Right Data Analysis Tool:

There is a wide variety of data analysis tools available in the market but the best-fitted tool for you will depend on the specific data set and the desired business outcome. When choosing a data analysis tool, it is essential to assess the specific features and capabilities of the tool, and the user’s needs should also be considered. For example, if you are looking to perform complex statistical analysis, then a statistical software package would be the best choice. On the other hand, if you are looking to create interactive dashboards, then a no-code data analytics platform would be a more suitable fit.

Below listed are some criteria that one should consider before choosing the right data analytics platform according to the requirements.

1. No-code Data Analytics Platform:

No-code data analytics platforms equip users with the capability to quickly analyze data with ease without having to write even a single line of code. This can save users a lot of time and effort by making data analysis more streamlined.

Some benefits provided by such data platforms are mentioned below:

No technical skills required: Analysis of data on these types of platforms can be performed by users of all skill types and different experience levels. Data analysis is made more accessible to individuals which allows them to benefit from it.

Supports Different Data types: Wide variety of data can be analyzed be it structured or unstructured, which makes these platforms more versatile.

Easy Integration: Easy integration with different sources is one of the best features provided by no-code data platforms.

Flexible pricing plans: No-code platforms provide scalability and are proven to be very cost-effective. This feature makes them useful for businesses of all sizes and stature.

If you are looking for a good and reliable no-code data analytics platform that has all these features then Sprinkle Data is the best option.

     2. Availability of Different Types of Charts:

Charts can help to picture data, and spot trends and patterns effortlessly. They help to make intricate data more coherent and can help individuals to make better decisions. Charts used with proper statistical techniques can be useful in making predictions about future behavior as well. They also can be used to interpret and find relationships between different variables and are useful in finding outliers in data. Different types of charts can be used to perform accurate analysis, some important chart types include:

Bar/column charts are one of the most typically used chart types and are especially helpful in comparing data points.

Line charts are used for depicting changes over time.

Pie charts are helpful in determining proportions across various categories

Scatter plots are useful for visualizing relationships between two numerical data points and are primarily used to identify trends and outliers in data.

Histograms are used to give information about the data distribution.

An area chart is based on a line chart and is primarily used to depict quantitative data by covering the area below the line.

Combo Chart is a combination of a line and a bar chart that depicts trends over time.

Funnel charts help to portray linear processes with sequential or interconnected phases in the analysis.

A map is a geographical chart type used to visualize data point density across different locations.

A stacked bar chart is a form of bar chart depicting comparisons of different data categories.

Charts are an integral part of any data analytics tool and can add meaning to the analysis. They help to communicate the conclusions of the analysis in a concise manner. So always choose a data analysis tool that has these charts with additional attributes like labels, a benchmark value, and different colors to easily differentiate.

All the chart types mentioned above are available in the Sprinkle Data analytics tool accessible with just a single click.

    3. Dashboard With a Good Visual Interface

A dashboard is a visual user interface that provides easy access to key metrics and consists of a sequence of charts, tables, and other visual elements that can be customized and systematized to provide insights into specific datasets with advantages like delivering visibility into an organization's performance in real time.

The key features that a dashboard should contain are mentioned below:

Interactivity: Dashboards with good interactivity permit users to filter and drill down into data for more detailed analysis.

Easily Editable layout: Customized dashboard show only the data that is relevant to the analysis.

Easy to share: Dashboards that can be easily shared with others to explore and analyze the data.

Less Runtime: A data analytics platform whose Dashboards take less time to run should be picked.

Monitoring: In case of a dashboard failure proper email alerts should be provided to the user with the reason for the error.

User-Friendly Interface: A dashboard with a user-friendly interface like drag and drop functionality is easy to use.

Live Dashboard: If you need to track data in real-time a live dashboard is the best option for your business.

If you are confused about which data analytics platform should be preferred to get all these features then you should prefer Sprinkle Data.

The best dashboard for your needs is the one that must follow all these criteria and will depend on the type of data you need to track, and the level of detail you need to acquire.

    4. Cost Efficient:

A cost-effective data analytics platform helps to save money on software and hardware. These tools can help organizations save money in a number of ways. By enabling organizations to understand their data better, these tools can help to recognize zones where costs can be decreased. Moreover, a platform with flexible and scalable pricing plans should be adopted to pay a reasonable price according to the requirements.

Sprinkle Data has a flexible pricing plan that is fully customizable according to the needs of users enabling them to save costs while performing high-level analytics.

Ultimately, the best way to choose the right data analysis tool is to consult with experts in the field and try different tools to see which one works best for your specific needs.

In this digital world, everyday great volumes of data are being generated from varied sources, and companies want to give this data a form and structure by assembling it in an organized way and at a unified place to use it to their advantage. They want to analyze their data further to get a good understanding and to make well-informed data-driven decisions. To bring meaning to this raw data, ETL tools play a significant role and help businesses to take data analytics to the next level.

There are several ETL tools available in the market which automate this whole process of building data-pipeline, managing and monitoring them. In this article, we will understand the whole process of ETL in detail and ETL tools that are best suited to automate your data pipelines for accurate analysis.

What is ETL?‍

ETL stands for “Extract, Transform and Load”. ETL is a process of extracting data from different data sources, cleansing and organizing it, and eventually, loading it to a target data warehouse or a Unified data repository.

Why ETL?

In today's data-centric world ETL plays a vital role in maintaining the integrity of a company by keeping its data up to date. To get the correct insight it is therefore important to perform ETL mainly due to the following reasons:

1. Data Volumes: The generated data has very high volume and velocity as many organizations have historical as well as real-time data being forged continuously from different sources.

2. Data Quality: The quality of the generated data is not exemplary as data is present in different formats like online feeds, online transactions, tables, images, excel, CSV, JSON, text files, etc. Data can be structured or unstructured, so to bring all different data formats to one homogeneous format performing the ETL process is highly needed.

To overcome these challenges many ETL tools are developed that make this process easy and efficient and help organizations combine their data by going through processes like de-duplicating, sorting, filtering, merging, reformatting, and transforming to make data ready for analysis.

ETL in detail:

1. Extract:

Extract is the first step of the ETL process that involves data being pulled from different data sources. It can extract data from the following sources listed below - 

Data Storage Platform & Data warehouses

Analytics tool

On-premise environment, hybrid, and cloud

CRM and ERP systems

Flat files, Email, and Web Pages

Manual data extraction can be highly time-consuming and error-prone, so to overcome these challenges automation of the Extraction process is the optimal solution.

Data Extraction: Different ways of extracting data.

1.1. Notification-based

In Notification-based extraction whenever data is updated, a notification is generated either through data replication or through webhooks (SaaS application). As soon as the notification is spawned data is pulled from the source. It is one of the easiest ways to detect any update but is not doable for some data sources that may not support generating a notification.

1.2. Incremental Extraction

In incremental extraction, only records that have been altered or updated are extracted/ingested. This extraction is majorly preferred for daily data ingestion as low-volume data is transferred making the daily data extraction process efficient. One major drawback of this extraction technique is once the extracted data is deleted it may not be detected.

1.3. Complete data extraction

In complete data extraction, the entire data is loaded. If a user wants to get full data or to ingest data for the first time then complete data extraction is preferred. The problem with this type of extraction is that if the data volume is massive it can be highly time-consuming.

Challenges in Data Extraction:

Data extraction is the first and foremost step in the ETL process, so we need to ensure the correctness of the extraction process before proceeding to the next step.

Data can be extracted using SQL or through API for SaaS, but this way may not be reliable as the API may change often or be poorly documented and different data sources can have various APIs. This is one of the major challenges faced during the data extraction process, other challenges are mentioned below.

Changing data formats

Increasing data volumes

Updates in source credentials.

Data issue with Null values

Change requests for new columns, dimensions, derivatives, and features.

2. TRANSFORM

Transform is the second step of the ETL process, in this raw data undergoes processing and modifications in the staging area. In this process, data is shaped according to the business use case and the business requirements.

The transformation layer consists of some of the following steps:

Removing duplicates, cleaning, filtering, sorting, validating, and affirming data.

Data inconsistencies and missing values are determined and terminated.

Data encryption or data protection as per industrial and government rules is implemented for security.

Formatting regulations are applied to match the schema of the target data repository

Unused data and anomalies are removed

Data Transformation: Different ways of transforming data

2.1. Multistage Data Transformation –

In multistage data transformation, data is moved to an intermediate area or staging area where all the transformation steps take place then eventually data is transferred to the final data warehouse where the business use cases are implemented for better decision-making.

2.2. In-Warehouse Data Transformation –

In ‘In-Warehouse Data Transformation ’,‍ data is first loaded into the data warehouse, and then all the subsequent data transformation steps are performed. This approach of transforming data is followed in the ELT process.

Challenges in Data Transformation

Data transformation is the most vital phase of the ETL process as it enhances data quality and guarantees data integrity yet there are some challenges faced when transforming data comes into play. Some challenges faced in transforming data are mentioned below:

Increasing data volumes makes it difficult to manage data and any transformation made can result in some data loss if not done properly.

The data transformation process is quite time-consuming and the chances of errors are also very high due to the manual effort.

More manpower and skills are required to efficiently perform the data transformation process which may even lead businesses to spend high.

3. LOAD

Once data is transformed, it is moved from the staging area to the target data warehouse which could be on the cloud or on-premise. Initially, the entire data is loaded, and then recurring loading of incremental data occurs. Sometimes, a full fetch of data takes place in the data warehouse to erase and replace old data with new one to overcome data inconsistencies.

Once data is loaded, it is optimized and aggregated to improve performance. The end goal is to quicken up the query span for the analytics team to perform accurate analysis in no time.

Data Loading: Considerations for error-free loading

Referential integrity constraint needs to be addressed effectively when new rows are inserted or a foreign key column is updated.

Partitions should be handled effectively for saving costs on data querying.

Indexes should be cleared before loading data into the target and rebuilt after data is loaded.

In Incremental loading, data should be in synchronization with the source system to avoid data ingestion failures.

Monitoring should be in place while loading the data so that any data loss creates warning alerts or notifications.

Challenges in Data Loading:

Data loading is the final step of the ETL process. This phase of ETL is responsible for the execution of correct data analysis. Therefore one must ensure that the data quality is up to the mark. The main challenge faced during data loading is mentioned below:

Data loss – While loading the data into the target system, there might be API unavailability, network congestion/failure or API credentials may expire these factors can result in complete data loss posing a greater threat to the business.

Overall Challenges of ETL

1. Code Issues

If ETL pipeline code is not optimized or manually coded, then such inefficiencies might affect the ETL process at any stage: It may cause problems while extracting data from the source, transforming data, or loading data into the target data warehouse and backtracking the issue can even be a tedious task.

2. Network Issues

The ETL process involves massive data transfer and processing on a daily basis which needs to be quick and efficient. So, the network needs to be fast and reliable, high latency of the network may create unexpected troubles in any of the stages and any network outage may even lead to data loss.

3. Lack of resources

Lack of any computing resources including storage, slow downloading, or lagging data processing in ETL may lead to fragmentation of your file system or create caches over a period of time.

4. Data Integrity

Since ETL involves collecting data from more than one source, if not done rightly, data might get corrupted which may create several inconsistencies and hence can cause data health reduction. So latest data needs to be carefully collected from sources and transformation techniques should be used accordingly.

5. Maintenance

In any organization increase in data corresponds to an increase in data sources so for business to maintain all their enormous data in a unified place more data connectors will keep on adding. So, while planning the ETL process, scalability, maintenance and the cost of maintenance should always be considered.

ETL vs ELT?

The main difference between ETL and ELT is the order of transformation, in ETL it happens before loading the data into the data warehouse however in ELT, data is first loaded and then its transformation takes place in the warehouse itself.

ELT Benefits over ETL

When dealing with high volumes of data ELT has a better advantage over ETL as transforming data before loading it into the data warehouse is an error-prone process and any mistake during transformation can cause complete data loss. Whereas in ELT data is first loaded into the warehouse and then it is transformed. So the chances of data loss are minimized in ELT as the data sits in the warehouse itself.

In ELT, not much planning is required by the team as compared to the ETL process. In ETL proper transformation rules need to be identified before the data loading process is executed which can be very time-consuming.

ELT is ideal for big data management systems and is adopted by organizations making use of cloud technologies, which is considered an ideal option for efficient querying.

For ETL, the process of data ingestion is very slow and inefficient, as the first data transformation takes place on a separate server, and after that data loading process starts. ELT does much faster data ingestion, as there is no data transfer to a secondary server for any restructuring. In fact, with ELT data can be loaded and transformed simultaneously.

ELT as compared to ETL is much faster, scalable, flexible, and efficient for large datasets which consist of both structured and unstructured data. ELT also helps to save data egress costs as before the transformation process the data sits in the data warehouse only.

Why do we need ETL tools?

ETL tools help to make the ETL process fast and efficient hence benefitting the businesses to stay one step ahead of their competitors. Some of the benefits of choosing the right ETL tool for your business are mentioned below:

1. Time Efficient: ETL tools permit us to collect, modify and integrate data automatically. Using ETL tools businesses can save more time as compared to time spent by bringing in data physically.

2. Low-code analysis: ETL tools generally offer low code/ no code functionality which helps to boost efficiency, requires less manual effort, and helps in keeping costs at bay.

3. Analyzing & Reporting: With the introduction of ETL tools analyzing data for reporting and dashboarding has become very easy. Data is available to us in a consolidated view and with the help of the right ETL tools, accurate analysis and reporting can be done.

4. Historical context: Businesses can benefit by analyzing the historical trends of their data to get its deep historical context and to predict some upcoming trends. There are many ETL tools available in the market that can efficiently analyze historical data in no time.

5. Data governance and ROI: It improves data accuracy and audit which is required for compliance with regulations and standards. It results in higher ROI for investments made in data teams.

There are numerous other benefits of ETL tools but the main challenge is to identify which ETL tool should be used by organizations to implement the right business use case according to their requirements.

Criteria for Choosing the Right ETL Tools

With the emergence of modern data-driven businesses, the space of ETL tools have also seen huge interest making the zone a crowded sector. But, with so many ETL Tools available in the market how should we go ahead with choosing the right ETL Tools for our businesses?

Below listed are some criteria that one should consider before choosing the right ETL tool according to the requirements.

1. In-built Connectors:

ETL tools with a high number of connectors should be preferred as they will provide more flexibility to businesses. Not only connectors but also, some of the widely used databases and applications in the industry must be available in the ETL tool that we choose.

2. Ease of Use and Clean User Interface:

ETL tools should be user-friendly, and an easy-to-understand user interface saves a lot of time and effort for its users and help them in using the tool hassle-free. Along with this, clear documentation to users should also be provided to get a better understanding of the selected ETL tool.

3. Scalable:

With the emergence of data-centric businesses, data tends to grow exponentially every day so to keep up with the cost a scalable ETL tool is of paramount importance for every business. We must choose an ETL tool with a good scalability option available to cater to business needs.

4. Error Handling:

Data Consistency and accuracy should be at the helm of any ETL tool that we choose for our business. In addition to this, the ETL tool should also have capabilities of smooth and efficient data transformation capabilities.

5. Real-time Data Ingestion and Monitoring:

ETL Tools with the capability to ingest data on a real-time basis from a wide range of sources should be highly considered for businesses that generate data every day. Apart from this, monitoring of the data ingestion and transformation process should be done accurately in that ETL tool to keep track of your data.

What is Snowflake?

Snowflake is a cloud data platform that supports multiple features, including handling large workloads. It gives secured access to data, offering scalability and better performance, and its easy-to-use platform is delivered as a service (Saas). Snowflake provides software as a service model which requires little maintenance helping customers to only focus on accumulating value from data without focusing much on platform maintenance often these qualities are offered by the right data warehouse. Traditional data warehouses were inflexible, pricey, and difficult to use but modern data warehouses came to the rescue revolutionizing the cloud technologies offering pricing models according to requirements rather than a subscription model preventing customers from overpaying and a utilization model where one uses resources according to needs, providing great scalability. One such data warehouse is Snowflake which provides numerous features and functionalities hence helping businesses to derive meaningful insights from data.

Why Prefer Snowflake?

A. Unique Architecture

‍Snowflake is famous for its unique architecture that gives it an upper edge as compared to other data warehouses and is considered to be the best platform to start off your business and grow exponentially. The best feature of snowflake is that it separates data storage, data processing, and data consumption by distinguishing them through layers whereas in other traditional warehouses there is only a single layer for storage and compute.

B. Efficiency

It saves effort and time by automatically managing all indexing and partitioning of tables not only this it also automatically separates compute on shared data allowing jobs to run in parallel.

C. Processes Standard SQL‍

Snowflake allows querying on data from the warehouse using the standard SQL and is ACID compliant.

D. Auto Scaling‍

The auto suspend feature in snowflake is one of the best that automatically suspends the warehouse if not in use.

What is Snowflake ETL?

ETL stands for extract, transform and load, in this data is collected from various sources and is unified in a target system preferably a data warehouse. If data from multiple sources is compiled and loaded into snowflake then it is called snowflake ETL.

‍5 Best Practices For Snowflake ETL

1. Always make use of auto suspend

2. Effectively manage costs

3. Make use of snowflake query profile

4. Transform data stepwise

5. Use data cloning

1. Always make use of auto suspend

When a warehouse is created, in snowflake you can set that warehouse to suspend after a certain amount of time. If the warehouse is inactive for that certain amount of time then snowflake automatically suspends it helping to keep costs at bay. By default the auto suspend option is enabled and it is a good practice to use auto suspend whenever possible.

If the warehouse is suspended and you run a query that is using a warehouse then it automatically resumes the warehouse also. This process is so fast and resumes the warehouse in no time.

2. Effectively manage costs

To save enormously on cost one needs to understand the pricing model offered by snowflake. Snowflake separates storage and compute costs. The storage cost is levied based on the average monthly storage consumption and the compute cost is set as per the total Snowflake credits consumed.

To effectively manage costs one should follow some of the best practices mentioned below :

Resource monitors should be set up: It will help to keep track of utilization needs.

Avoid using Select * statements whenever possible: Suppose a user wants to have a look at the data then instead of viewing the whole data one can just take a glimpse of it.

Setting up alerts: Sometimes for non-snowflake users reader accounts are created as a result users can run a query on the data which unnecessarily shoots up the consumption costs. It is considered to set up alerts for reader accounts to keep track of incurring costs to monitor those accounts.

3. Make use of Snowflake Query Profile

Query profile is a powerful tool that helps in diagnosing a query that provides its execution details. It gives information about the performance and behavior of a query.

It is considered to use Snowflake’s Query Profile tool to analyze issues in queries that are running slow. Query Profile lets you examine how Snowflake ran your query and what steps in the process are causing the query to slow down

4. Transform data stepwise

You should always refrain from using complex SQL queries and always try to write simple queries as it can be difficult to maintain the code. Instead, you can write SQL code in chunks that are a lot easier to understand, and maintain, and they can be proved to be more time efficient. Writing queries in small chunks and then combining them afterward can enhance the warehouse performance and it can even give rise to time-efficient querying.

5. Use Data Cloning

Cloning is a feature in snowflake that creates a copy of the database, schema, or table. Cloning forms a derived replica of that object that shares the storage. This cloning feature is very convenient when creating instant backups. No extra cost is involved while using this feature unless and until any changes are made to the original source.

Conclusion :

Snowflake has a unique 3-layer architecture and is a modern data warehouse to make the most of its services, these practices can prove to be helpful to you.

Snowflake supports a wide variety of features that are used to apply for implementing the right data analytics use cases and to help businesses make better-informed decisions. Snowflake is one of the most admired data warehouses used and trusted by millions of users today. It provides flexibility, accessibility, and scalability helping businesses to manage their data easily in no time.

TL;DR

Always use auto suspend when the warehouse is not being used, this reduces consumption and utilization.

Set up alerts for new reader accounts to bring costs down

Avoid using select * statements to view the data instead use a query that is fast and that will not require scanning all your data.

Always keep an eye on the resource monitor to calculate costs and check whether the threshold limit is reached.

Use the Snowflake query profile to keep a track of slow processing queries.

Transform data in steps instead of writing a single complex code snippet.

Use data cloning to copy the tables, schemas, or database.

Introduction

Throughout the years, DevOps has proved to be a successful practice in optimizing the product delivery cycle. As years passed by and as enterprises throughout the world focused on building a data-driven culture, it was necessary to build it in a proper manner to reap the most benefits from one’s business data. Instead of optimizing with mere assumptions and predictions, these business data provided users with factual insights for best decision making. 

Before we dive deep on how different DataOps is from DevOps, let me clue you in with a crisp explanation.

DevOps is the transformation in the delivery capability of development and software teams whereas DataOps focuses much on the transforming intelligence systems and analytic models by data analysts and data engineers. 

DevOps is a synergy of development, IT operations, and engineering teams with the main idea to reduce cost and time spent on the development and release cycle. However, DataOps works one level further. It’s nothing but dealing with Data. The data teams work with teams of various levels to acquire data, transform, model, and obtain actionable insights. 

This consistent collaboration between the teams helps in continuous integrations and delivery with automation and iterative process in workflows.

Similar to how DevOps transformed the way in which the software development cycle works, DataOps is also changing the primitive practices of handling data by implementing DevOps principles.‍‍ 

The workflow of DevOps and DataOps

Data and analytics closely deal with integrations, business, and insights whereas DevOps practices are mostly about software development, feature upgrades, and deploying fixes. Although they are different by far, when it comes to dealing with the element they work with, the core operational strategy is pretty much the same. 

DataOps is not very much different when compared to DevOps, for example, the goal setting, developing, building, testing, and deploying are part of the DevOps operations whereas, in DataOps, goal setting, gathering resources, orchestrating, modeling, monitoring, and studying are the steps involved.

The principles involved with DevOps and DataOps

DevOps is often claimed to be a pattern of collaborative learning. This collaborative learning is enabled by short and swift feedback loops which is much more economical than the primitive methods. This structure and discipline in consistent sprints are facilitated by applying Agile principles across the organization.

When it comes to DataOps, Data happens to be that differentiating element although both the practices use Agile methodology. In few instances, the sprints might go on and the desired outcomes couldn’t be developed over a period of time due to disparate teams, few processes might be stagnant before reaching out a tester or the person who deploys it.

Minimizing the steps from the feedback loop and the delivery cycle is basically a reflection of the proper real-time connectivity within teams. The real-time cross-functionality between teams helps with real-time operations like feedbacks, goal setting, etc. 

However, when dealing with data acquisition, Lean principles happen to be the best way to extract the more out of your business data. A process control strategy where the acquired data is put under a series of quality checks before modeling. Any data anomalies that disrupt the flow in such operations need to be filtered out so that it wouldn’t damage the end-users confidence in data and the insights they observe.

This makes DataOps a logical successor of the DevOps initiatives as it inherits the Agile & Lean benefits for people who deal with Data.

A brief differentiation between DevOps and DataOps

Introduction

In today’s modern world where data is considered to be the most valuable company’s asset, there is a need to protect it from data breaches and damages. As we know with every problem comes a solution, so for this issue we have modern data warehouses with the best built-in functionalities to protect it from attacks and are used to manage the data effectively, one such warehouse is: Google BigQuery.

Google BigQuery is a fully controlled enterprise data warehouse that aids in handling and examining data with built-in features like machine learning, data analysis, and business intelligence. Many firms use this data warehouse to implement data analytics use cases to help them make better informed decisions. It is a highly scalable and cost-effective data warehouse that supports many features directing businesses to progress in the right direction.

Though BigQuery is fast and affordable, it can consume a lot of processing power if not used properly. To use GoogleBig query the right way and to make the most out of your investment in this, here are five best practices that should be followed.

Selecting the correct Data format

Using partitioning for better performance

Using clustering for better performance

Managing workloads using reservations

Using logs the right way

1. Selecting the correct data format

For BigQuery ETL, there is a wide range of file formats that can help you in smooth data ingestion without any hassles. Proper data ingestion format should be the first and foremost thing to perform any data operations, and big query provides this flexibility by providing multiple data formats. Choosing the correct file format solely depends on one's requirements.

If your aim is to optimize the load pace then the Avro file format is most favoured. This is basically a binary row-based format that allows us to split data and then read it in parallel. Businesses prefer using Avro file format in compressed form which is supposed to be quicker in terms of magnitude as compared with the other techniques of ingesting data into big query.

Parquet and ORC are binary-based and column-oriented data formats that are relatively faster but are not as fast as loading data from Avro file formats.

Loading data from CSV and JSON compressed files is going to be more overhead and slower than loading data from any other formats as it takes more computation time. This is Because compression of Gzip is not breakable. First, it takes the Gzip file and then loads it onto a slot within the big query, then decompression of the file is done, and finally, it parallelises the load afterward. This process is a little slower and consumes more time hence impacting business agility.

The image below shows the file formats according to the speed of data loading.

2. Use of Partitioning for better performance

In BigQuery, partitions help in splitting the data on disk based on the ingestion time or on the basis of the column value of the data. It helps to divide large tables into smaller tables creating a partition. Data for each partition is stored singly associated with its own metadata for faster recognition. Partitioning can help enhance the query performance as when a query related to a specific partition value is run in BigQuery then it only scans the partition value that is provided instead of the entire table, saving time and enhancing query performance.

In partitioning, data is divided into smaller tables for low cardinality columns ie columns having few unique values like gender, boolean columns, etc that is proved to be time-efficient. Partitioning helps to keep costs down by avoiding the scanning of large amounts of data and also helps in storage optimization.

A good practice is whenever you are dealing with a fact table or a temporary table it is advised to utilize a partition column by specifying the partition key on the date dimension, it speeds up the query and provides optimal performance.

3. Use of clustering for better performance

‍Clustering is basically the ordering of data within a partition. Making a cluster allows BigQuery to keep similar data conjointly and boost performance by scanning just a few records when a query is run. If clustering is used the right way it can provide many performance-related benefits.

A good convention is to use clustering wherever possible as it will restrict the amount of data scanned per second. For example, let’s take a transaction fact table partitioned on the transaction date column, and adding clustering on columns like country, state, ID, etc will result in dynamic and time-efficient queries over that table.

A combination of partitioning and clustering used together is also highly recommended to optimize your query performance by identifying the low and high cardinality columns for partitioning and clustering.‍

4. Managing workloads using reservations

BigQuery reservations bring down the costs, are highly scalable, and provide great flexibility to switch between two pricing models to purchase slots:

‍1. On-demand pricing:

The default pricing model that gives up to 2000 concurrent slots.

2. Flat rate pricing:

Calculated slots are purchased and reserved according to requirements.

A slot is basically a virtual CPU used by BigQuery to execute SQL queries. The number of slots is automatically evaluated by BigQuery according to query size and complexity.

To prioritize a query reservations are preferred. In this, if a query has a higher priority then more slots should be assigned to it for quick response time and to effectively manage different workloads.

 5. Using logs the right way:

BigQuery keeps trace of all admin activities via log files and it is really beneficial if you want to perform an analysis of your system to keep a check on its proper functioning.

Basically, there are some good practices related to logs :

1. Export data access log files to BigQuery :

It is recommended to keep all your audit log files in a unified place as when all your log files are sitting together it becomes a lot easier to query on them.

2. Visualizing audit logs using data studio :

Visualizing the audit log files can actually help to track down the used resources and even monitor the amount of money spent on them.

This practice can really help in the following ways:

A. Monitor your spending patterns

B. Cost bifurcation by project

C. Making queries more efficient and less space-consuming hence can help in cutting down on cost 

For cost controlling :

Avoid running SELECT  * queries whenever possible.

View costs by visualization and query audit logs

Price query before running them.

Using LIMIT will not lower your costs.

Keep an eye on the storage costs as well

‍For query optimization :

Use clustering and partitioning wherever possible

Avoid using joins and subqueries frequently

We have analyzed the 5 best free or affordable SQL reporting tools and open source dashboard reporting tools for Startups and SMEs

Sprinkle Data Metabase Chartio Redash Mode Analytics

Introduction

We can see a growing pattern of analysing one’s business data. This trend starts right from the biggest of enterprises to small startups. However, very big businesses could afford any analytic or reporting tools. Whereas SMEs and Startups find it hard to identify the best tool for their requirements which also fits into their budget.

We have analyzed the 5 best free or affordable SQL reporting tools and open source dashboard reporting tools for Startups and SMEs

Sprinkle Data

Metabase

Chartio

Redash

Mode Analytics

Sprinkle Data

Sprinkle is an all in one no-code data analytics tool. Provides ability to integrate, blend and model data without writing code.

Pricing: Free plan, with paid plans starting from $200-$500

Pros:

Does not require data to be loaded into a BI tool. Works directly on the data warehouse.

Capable of connecting to a vast set of data sources like MySQL, Kafka, Kinesis, PostgreSQL, Hubspot, MongoDB, etc. See the complete list here Supported Data Sources

Different datasets can be joined together using the data modeling interface without coding. Supports semi-structured data.

The segmentation feature enables business users to do Exploratory data analysis on their own without depending on the analytics team to build reports.

Sprinkle allows users to enable Slack and Email notifications and it also has embedded analytic features, where any users can access the dashboard without accessing the tool.

Cons:

Downloading charts in PDF format not supported

Currently Git integration is not supported

Row level access control available only in higher plans

Metabase

Metabase is an open source graphical interface that helps in creating business intelligence and analytics graphs seamlessly.

Pricing: Starts at $100/month to $300/month business plans, Enterprise edition is tailored for your requirements.

Pros:

An open-source tool, preferably used by non-technical users as it doesn’t require complex coding.

Metabase supports multiple relational and non relational databases as datasources. For example, Amazon Redshift, Google BigQuery, MongoDB, MySQL, Snowflake, etc. See the full list here Data sources

Zero-code required for creating reports, dashboards and visualizations, and for complex queries, SQL can be used

With pulses, updates can be sent on specific queries to Slack channel or through Email.

Cons:

Doesn’t allow joining of data, metabase works well only with a single data source and struggles when it comes to multiple data sources.

Data source integrations are very limited when compared to competitors.

Users prefer having API integrations to most third party applications.

Chartio

Chartio is a data exploration solution for all, this cloud based BI tool connects with your data in order to provide business analytics in the form of dashboards and charts.

Pricing: Starts at $400/month and goes upto $900/month. Users can also request a quote for the enterprise version.

Pros:

Chartio is capable of data transformations, data blending and also has data sharing capabilities.

The tool is well suited for advanced users as the drag and drop functionality are not as easy when compared to competitors.

Chartio supports hundreds of relational and non relational databases. Access the full list here Data sources

The tools allow users to combine and compare reports from various sources, say, moosend’s email campaign history can be compared to Kissmetrics’ data.

Cons:

The UI is not friendly, and takes a tad bit of time for users to get used.

Doesn’t support embedded analytics in the base package.

Not the best in class visualization features and the appearance isn’t much appealing.

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Redash

Redash is a Business Intelligence tool that runs SQL queries and builds web-based visualization and dashboards as per your requirements.

Pricing: Starts at $49/month and goes up to $450/month.

Pros:

Redash has a vast array of visualizations that users can create and it supports most data sources. Access the complete list here Supported Data Sources.

Suitable for advanced users; Any query results can be used as data sources which can be joint with different databases to make advanced reports.

The tool has an interactive query editor which shares both the dataset and the query that generated it.

With this tool, auto notifications can be enabled, reminders can be set easily and alerts on breaching the preset value.

Cons:

Everything needs SQL querying, this makes it hard for the non technical users.

Old school data visualization feature and also very limited configurations.

Works best when a single SQL data source is used, finds it hard to work when multiple data sources are used.

Mode Analytics

Mode is a collaborative self-served BI tool that works with SQL, R, Python, etc. This tool helps with dashboarding and deriving insights from it.

Pricing: Mode Studio is free whereas Mode Business allows a 14 day trial, both have different sets of options.

Pros:

Runs on the browser. Simple interface, drag and drop functionality and contains a large number of built-in features.

The tool is capable of running complex queries using SQL, Python and R.

More than 60 libraries of Python and R are available. Data analysis of Python and R can be done in Mode Notebooks.

Cons:

Hard to adapt for business users as it requires at least basic SQL to create even the simplest of charts.

As dashboards keep scaling in size, the drag and drop functionality could be tedious.

For basic data visualization the options are quite limited. For example, legend color cannot be changed without either re-writing your SQL code or using HTML, same goes for interchanging x/y axis inversion, advanced manipulations have to be done.

‍

Introduction

ETL happens to be the most important process of data warehousing and obtaining actionable insights. Most tools in the market are unique in their own way where one size fits all approach doesn’t work here. Identifying which tool suits your business’s data requirements is the key.

Every tool handles data in different ways and offers various types of functionality in terms of connectors, transformations, pipelines, deployment, visualization, etc. In such a case, we have picked the five best ETL/ELT tools in the market. Study, compare and decide for yourself.

Sprinkledata

Sprinkle is an ELT tool, end to end data integration and analytics platform enabling users to automate the complete journey of collating data from various data sources to ingesting them into a preferred data warehouse to building reports. And all these processes happen in real-time. 

Pros:

 Zero-code Ingestion: Automatic schema discovery and mapping of data types to the warehouse types. Supports JSON data as well

 Built in Data Visualization & BI: Does not require separate BI tool

 No proprietary Transformation code: Sprinkle does ELT (offer much more flexibility and scaling than the legacy ETL). Write transformations in SQL or python

 Jupyter Notebook: Integrated environment to do EDA and production-ize ML pipeline from the Jupyter notebooks in single-click

 No data leaves customer’s network: Sprinkle offers Enterprise version that can run on customer’s VM within the customer’s Cloud

 Cost Effective: Payment model is not based on the number of rows, as the rows are unlimited with Sprinkle, users wouldn't have to worry about the costs as their business and data scales

Cons:

Some of the chart types not supported yet 

Informatica

Power center is a tool from Informatica’s pool of tools for cloud data management. Informatica Power Center focuses on providing agility in data integration. It serves as a core to data integrations where the focus is to reduce manual hand coding and accelerating the automation.

Pros:

 Reading, Transforming and Loading Data to and from databases, and more importantly the speed of these operations

 Good tool to manage legacy data and to make it compatible with modern applications

 PowerCenter also has other additional features like the ability to profile data, visualize graphical data flow, etc

Cons:

 The built-in scheduling tool has many constraints such as handling Unix/VB scripts etc. Most enterprises use third party tools for this

 Not very user friendly GUI and the management of EII processes (like Restful or SOAP) are not well supported by the on-premise solution.

 The licensing fee is huge, businesses with small budgets can’t usually opt this tool

‍Alteryx

Alteryx is an end-to-end data analytics platform for organizations that allow analysts to solve complex business problems in a jiffy. Code-free, deployable analytics and the scaling analytics for organizations transforms into performance, security and governance.

 Pros:

 Cleaning data sets, transforming from one format to another are the key attributes of Alteryx

 The tool has built in methods for data preparation and merging data sets

 Scheduling the flows and reporting, Python Integration helps to ease out the process

 Good Interfacing where it helps with quick drag and drop functionality to create a workflow

Cons:

 Data visualization is a drawback, needs an external tool like Tableau or Power BI

 Too expensive, although training and support is freely accessible

 The API connections are limited. Although they run a macro which can be created and connected to any API, it’s not direct

 Alteryx doesn’t run on MacOS, it only runs with a virtual machine product which emulates windows within a MacOS

‍SSIS

SQL Server Integration Services is an enterprise-level data integration and data transformation platform. SQL Server Integration Service helps solve complex business problems by collecting data from various sources, loading into the data warehouse, cleansing and managing SQL Server objects and data.

Pros:

 A tool for complex transformations, compiling data from various sources, and structure exception handling

 SSIS provides unique transformations and is tightly integrated with Microsoft Visual Studio and SQL Server

 Has a complete suite of configurable tools and also enables source system connectivity (API's, SQL DB's, Olap Cubes, etc)

Cons:

 When users work with multiple packages in parallel, the SSIS memory usage is high and it conflicts with SQL

 Doesn’t work efficiently with JSON-related data, Unstructured datasets can be challenging to work with

 Has major issues with version control, happens to be a pain point where users deal with many different sources

‍Fivetran

Fivetran is a cloud based ETL tool which is known for its large number of custom data source integrations. Fivetran has resource driven schemas which are prebuilt, automated and ready-to-query.

 Pros:

 Fivetran provides post-load transformation functionality for modeling from SQL-based transformations

 Fast setup; plug and play integration with any application

 Automatic schema migrations, requires zero coding for transformations

 Only the active rows are considered when it comes to pricing

Cons:

 Requires an external tool for visualization and BI part as it does just the ELT part

 Fivetran only offers one direction data sync and doesn’t provide two way directional sync

 Fixed schemas; The users cannot control the data sent to any specific destination